Systems and methods to detect rare mutations and copy number variation

ABSTRACT

The present disclosure provides a system and method for the detection of rare mutations and copy number variations in cell free polynucleotides. Generally, the systems and methods comprise sample preparation, or the extraction and isolation of cell free polynucleotide sequences from a bodily fluid; subsequent sequencing of cell free polynucleotides by techniques known in the art; and application of bioinformatics tools to detect rare mutations and copy number variations as compared to a reference. The systems and methods also may contain a database or collection of different rare mutations or copy number variation profiles of different diseases, to be used as additional references in aiding detection of rare mutations, copy number variation profiling or general genetic profiling of a disease.

CROSS-REFERENCE

This application is a continuation application of U.S. patentapplication Ser. No. 14/425,189, filed Mar. 2, 2015, which applicationis a national stage entry of PCT/US2013/058061, filed on Sep. 4, 2013,which application claims priority to U.S. Provisional Patent ApplicationNo. 61/696,734, filed Sep. 4, 2012, U.S. Provisional Patent ApplicationNo. 61/704,400, filed Sep. 21, 2012, U.S. Provisional Patent ApplicationNo. 61/793,997, filed Mar. 15, 2013, and U.S. Provisional PatentApplication No. 61/845,987, filed Jul. 13, 2013, each of which isentirely incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

The detection and quantification of polynucleotides is important formolecular biology and medical applications such as diagnostics. Genetictesting is particularly useful for a number of diagnostic methods. Forexample, disorders that are caused by rare genetic alterations (e.g.,sequence variants) or changes in epigenetic markers, such as cancer andpartial or complete aneuploidy, may be detected or more accuratelycharacterized with DNA sequence information.

Early detection and monitoring of genetic diseases, such as cancer isoften useful and needed in the successful treatment or management of thedisease. One approach may include the monitoring of a sample derivedfrom cell free nucleic acids, a population of polynucleotides that canbe found in different types of bodily fluids. In some cases, disease maybe characterized or detected based on detection of genetic aberrations,such as a change in copy number variation and/or sequence variation ofone or more nucleic acid sequences, or the development of other certainrare genetic alterations. Cell free DNA (“cfDNA”) has been known in theart for decades, and may contain genetic aberrations associated with aparticular disease. With improvements in sequencing and techniques tomanipulate nucleic acids, there is a need in the art for improvedmethods and systems for using cell free DNA to detect and monitordisease.

SUMMARY OF THE INVENTION

The disclosure provides for a method for detecting copy number variationcomprising: a) sequencing extracellular polynucleotides from a bodilysample from a subject, wherein each of the extracellular polynucleotideare optionally attached to unique barcodes; b) filtering out reads thatfail to meet a set threshold; c) mapping sequence reads obtained fromstep (a) to a reference sequence; d) quantifying/counting mapped readsin two or more predefined regions of the reference sequence; e)determining a copy number variation in one or more of the predefinedregions by (i) normalizing the number of reads in the predefined regionsto each other and/or the number of unique barcodes in the predefinedregions to each other; and (ii) comparing the normalized numbersobtained in step (i) to normalized numbers obtained from a controlsample.

The disclosure also provides for a method for detecting a rare mutationin a cell-free or substantially cell free sample obtained from a subjectcomprising: a) sequencing extracellular polynucleotides from a bodilysample from a subject, wherein each of the extracellular polynucleotidegenerate a plurality of sequencing reads; b) sequencing extracellularpolynucleotides from a bodily sample from a subject, wherein each of theextracellular polynucleotide generate a plurality of sequencing reads;sequencing extracellular polynucleotides from a bodily sample from asubject, wherein each of the extracellular polynucleotide generate aplurality of sequencing reads; c) filtering out reads that fail to meeta set threshold; d) mapping sequence reads derived from the sequencingonto a reference sequence; e) identifying a subset of mapped sequencereads that align with a variant of the reference sequence at eachmappable base position; f) for each mappable base position, calculatinga ratio of (a) a number of mapped sequence reads that include a variantas compared to the reference sequence, to (b) a number of total sequencereads for each mappable base position; g) normalizing the ratios orfrequency of variance for each mappable base position and determiningpotential rare variant(s) or mutation(s); h) and comparing the resultingnumber for each of the regions with potential rare variant(s) ormutation(s) to similarly derived numbers from a reference sample.

Additionally, the disclosure also provides for a method ofcharacterizing the heterogeneity of an abnormal condition in a subject,the method comprising generating a genetic profile of extracellularpolynucleotides in the subject, wherein the genetic profile comprises aplurality of data resulting from copy number variation and/or other raremutation (e.g., genetic alteration) analyses.

In some embodiments, the prevalence/concentration of each rare variantidentified in the subject is reported and quantified simultaneously. Inother embodiments, a confidence score, regarding theprevalence/concentrations of rare variants in the subject, is reported.

In some embodiments, extracellular polynucleotides comprise DNA. Inother embodiments, extracellular polynucleotides comprise RNA.Polynucleotides may be fragments or fragmented after isolation.Additionally, the disclosure provides for a method for circulatingnucleic acid isolation and extraction.

In some embodiments, extracellular polynucleotides are isolated from abodily sample that may be selected from a group consisting of blood,plasma, serum, urine, saliva, mucosal excretions, sputum, stool andtears.

In some embodiments, the methods of the disclosure also comprise a stepof determining the percent of sequences having copy number variation orother rare genetic alteration (e.g., sequence variants) in said bodilysample.

In some embodiments, the percent of sequences having copy numbervariation in said bodily sample is determined by calculating thepercentage of predefined regions with an amount of polynucleotides aboveor below a predetermined threshold.

In some embodiments, bodily fluids are drawn from a subject suspected ofhaving an abnormal condition which may be selected from the groupconsisting of, mutations, rare mutations, single nucleotide variants,indels, copy number variations, transversions, translocations,inversion, deletions, aneuploidy, partial aneuploidy, polyploidy,chromosomal instability, chromosomal structure alterations, genefusions, chromosome fusions, gene truncations, gene amplification, geneduplications, chromosomal lesions, DNA lesions, abnormal changes innucleic acid chemical modifications, abnormal changes in epigeneticpatterns, abnormal changes in nucleic acid methylation infection andcancer.

In some embodiments, the subject may be a pregnant female in which theabnormal condition may be a fetal abnormality selected from the groupconsisting of, single nucleotide variants, indels, copy numbervariations, transversions, translocations, inversion, deletions,aneuploidy, partial aneuploidy, polyploidy, chromosomal instability,chromosomal structure alterations, gene fusions, chromosome fusions,gene truncations, gene amplification, gene duplications, chromosomallesions, DNA lesions, abnormal changes in nucleic acid chemicalmodifications, abnormal changes in epigenetic patterns, abnormal changesin nucleic acid methylation infection and cancer

In some embodiments, the method may comprise comprising attaching one ormore barcodes to the extracellular polynucleotides or fragments thereofprior to sequencing, in which the barcodes comprise are unique. In otherembodiments barcodes attached to extracellular polynucleotides orfragments thereof prior to sequencing are not unique.

In some embodiments, the methods of the disclosure may compriseselectively enriching regions from the subject's genome or transcriptomeprior to sequencing. In other embodiments the methods of the disclosurecomprise selectively enriching regions from the subject's genome ortranscriptome prior to sequencing. In other embodiments the methods ofthe disclosure comprise non-selectively enriching regions from thesubject's genome or transcriptome prior to sequencing.

Further, the methods of the disclosure comprise attaching one or morebarcodes to the extracellular polynucleotides or fragments thereof priorto any amplification or enrichment step.

In some embodiments, the barcode is a polynucleotide, which may furthercomprise random sequence or a fixed or semi-random set ofoligonucleotides that in combination with the diversity of moleculessequenced from a select region enables identification of uniquemolecules and be at least a 3, 5, 10, 15, 20 25, 30, 35, 40, 45, or 50mer base pairs in length.

In some embodiments, extracellular polynucleotides or fragments thereofmay be amplified. In some embodiments amplification comprises globalamplification or whole genome amplification.

In some embodiments, sequence reads of unique identity may be detectedbased on sequence information at the beginning (start) and end (stop)regions of the sequence read and the length of the sequence read. Inother embodiments sequence molecules of unique identity are detectedbased on sequence information at the beginning (start) and end (stop)regions of the sequence read, the length of the sequence read andattachment of a barcode.

In some embodiments, amplification comprises selective amplification,non-selective amplification, suppression amplification or subtractiveenrichment.

In some embodiments, the methods of the disclosure comprise removing asubset of the reads from further analysis prior to quantifying orenumerating reads.

In some embodiments, the method may comprise filtering out reads with anaccuracy or quality score of less than a threshold, e.g., 90%, 99%,99.9%, or 99.99% and/or mapping score less than a threshold, e.g., 90%,99%, 99.9% or 99.99%. In other embodiments, methods of the disclosurecomprise filtering reads with a quality score lower than a setthreshold.

In some embodiments, predefined regions are uniform or substantiallyuniform in size, about 10 kb, 20 kb, 30 kb 40 kb, 50 kb, 60 kb, 70 kb,80 kb, 90 kb, or 100 kb in size. In some embodiments, at least 50, 100,200, 500, 1000, 2000, 5000, 10,000, 20,000, or 50,000 regions areanalyzed.

In some embodiments, a genetic variant, rare mutation or copy numbervariation occurs in a region of the genome selected from the groupconsisting of gene fusions, gene duplications, gene deletions, genetranslocations, microsatellite regions, gene fragments or combinationthereof. In other embodiments a genetic variant, rare mutation, or copynumber variation occurs in a region of the genome selected from thegroup consisting of genes, oncogenes, tumor suppressor genes, promoters,regulatory sequence elements, or combination thereof. In someembodiments the variant is a nucleotide variant, single basesubstitution, or small indel, transversion, translocation, inversion,deletion, truncation or gene truncation about 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 15 or 20 nucleotides in length.

In some embodiments, the method comprisescorrecting/normalizing/adjusting the quantity of mapped reads using thebarcodes or unique properties of individual reads.

In some embodiments, enumerating the reads is performed throughenumeration of unique barcodes in each of the predefined regions andnormalizing those numbers across at least a subset of predefined regionsthat were sequenced. In some embodiments, samples at succeeding timeintervals from the same subject are analyzed and compared to previoussample results. The method of the disclosure may further comprisedetermining partial copy number variation frequency, loss ofheterozygosity, gene expression analysis, epigenetic analysis andhypermethylation analysis after amplifying the barcode-attachedextracellular polynucleotides.

In some embodiments, copy number variation and rare mutation analysis isdetermined in a cell-free or substantially cell free sample obtainedfrom a subject using multiplex sequencing, comprising performing over10,000 sequencing reactions; simultaneously sequencing at least 10,000different reads; or performing data analysis on at least 10,000different reads across the genome. The method may comprise multiplexsequencing comprising performing data analysis on at least 10,000different reads across the genome. The method may further compriseenumerating sequenced reads that are uniquely identifiable.

In some embodiments, the methods of the disclosure comprise normalizingand detection is performed using one or more of hidden markov, dynamicprogramming, support vector machine, Bayesian network, trellis decoding,Viterbi decoding, expectation maximization, Kalman filtering, or neuralnetwork methodologies.

In some embodiments the methods of the disclosure comprise monitoringdisease progression, monitoring residual disease, monitoring therapy,diagnosing a condition, prognosing a condition, or selecting a therapybased on discovered variants.

In some embodiments, a therapy is modified based on the most recentsample analysis. Further, the methods of the disclosure compriseinferring the genetic profile of a tumor, infection or other tissueabnormality. In some embodiments growth, remission or evolution of atumor, infection or other tissue abnormality is monitored. In someembodiments the subject's immune system are analyzed and monitored atsingle instances or over time.

In some embodiments, the methods of the disclosure compriseidentification of a variant that is followed up through an imaging test(e.g., CT, PET-CT, MRI, X-ray, ultrasound) for localization of thetissue abnormality suspected of causing the identified variant.

In some embodiments, the methods of the disclosure comprise use ofgenetic data obtained from a tissue or tumor biopsy from the samepatient. In some embodiments, whereby the phylogenetics of a tumor,infection or other tissue abnormality is inferred.

In some embodiments, the methods of the disclosure comprise performingpopulation-based no-calling and identification of low-confidenceregions. In some embodiments, obtaining the measurement data for thesequence coverage comprises measuring sequence coverage depth at everyposition of the genome. In some embodiments correcting the measurementdata for the sequence coverage bias comprises calculatingwindow-averaged coverage. In some embodiments correcting the measurementdata for the sequence coverage bias comprises performing adjustments toaccount for GC bias in the library construction and sequencing process.In some embodiments correcting the measurement data for the sequencecoverage bias comprises performing adjustments based on additionalweighting factor associated with individual mappings to compensate forbias.

In some embodiments, the methods of the disclosure compriseextracellular polynucleotide derived from a diseased cell origin. Insome embodiments, the extracellular polynucleotide is derived from ahealthy cell origin.

The disclosure also provides for a system comprising a computer readablemedium for performing the following steps: selecting predefined regionsin a genome; enumerating number of sequence reads in the predefinedregions; normalizing the number of sequence reads across the predefinedregions; and determining percent of copy number variation in thepredefined regions. In some embodiments, the entirety of the genome orat least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the genome isanalyzed. In some embodiments, computer readable medium provides data onpercent cancer DNA or RNA in plasma or serum to the end user.

In some embodiments, the amount of genetic variation, such aspolymorphisms or causal variants is analyzed. In some embodiments, thepresence or absence of genetic alterations is detected.

The disclosure also provides for a method for detecting a rare mutationin a cell-free or a substantially cell free sample obtained from asubject comprising: a) sequencing extracellular polynucleotides from abodily sample from a subject, wherein each of the extracellularpolynucleotides generate a plurality of sequencing reads; b) filteringout reads that fail to meet a set threshold; c) mapping sequence readsderived from the sequencing onto a reference sequence; d) identifying asubset of mapped sequence reads that align with a variant of thereference sequence at each mappable base position; e) for each mappablebase position, calculating a ratio of (a) a number of mapped sequencereads that include a variant as compared to the reference sequence, to(b) a number of total sequence reads for each mappable base position; f)normalizing the ratios or frequency of variance for each mappable baseposition and determining potential rare variant(s) or other geneticalteration(s); and g) comparing the resulting number for each of theregions

This disclosure also provides for a method comprising: a. providing atleast one set of tagged parent polynucleotides, and for each set oftagged parent polynucleotides; b. amplifying the tagged parentpolynucleotides in the set to produce a corresponding set of amplifiedprogeny polynucleotides; c. sequencing a subset (including a propersubset) of the set of amplified progeny polynucleotides, to produce aset of sequencing reads; and d. collapsing the set of sequencing readsto generate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides. In certain embodiments the method further comprises: e.analyzing the set of consensus sequences for each set of tagged parentmolecules.

In some embodiments each polynucleotide in a set is mappable to areference sequence.

In some embodiments the method comprises providing a plurality of setsof tagged parent polynucleotides, wherein each set is mappable to adifferent reference sequence.

In some embodiments the method further comprises converting initialstarting genetic material into the tagged parent polynucleotides.

In some embodiments the initial starting genetic material comprises nomore than 100 ng of polynucleotides.

In some embodiments the method comprises bottlenecking the initialstarting genetic material prior to converting.

In some embodiments the method comprises converting the initial startinggenetic material into tagged parent polynucleotides with a conversionefficiency of at least 10%, at least 20%, at least 30%, at least 40%, atleast 50%, at least 60%, at least 80% or at least 90%.

In some embodiments converting comprises any of blunt-end ligation,sticky end ligation, molecular inversion probes, PCR, ligation-basedPCR, single strand ligation and single strand circularization.

In some embodiments the initial starting genetic material is cell-freenucleic acid.

In some embodiments a plurality of the reference sequences are from thesame genome.

In some embodiments each tagged parent polynucleotide in the set isuniquely tagged.

In some embodiments the tags are non-unique.

In some embodiments the generation of consensus sequences is based oninformation from the tag and/or at least one of sequence information atthe beginning (start) region of the sequence read, the end (stop)regions of the sequence read and the length of the sequence read.

In some embodiments the method comprises sequencing a subset of the setof amplified progeny polynucleotides sufficient to produce sequencereads for at least one progeny from of each of at least 20%, at least30%, at least 40%, at least 50%, at least 60%, at least 70%, at least80%, at least 90% at least 95%, at least 98%, at least 99%, at least99.9% or at least 99.99% of unique polynucleotides in the set of taggedparent polynucleotides.

In some embodiments the at least one progeny is a plurality of progeny,e.g., at least 2, at least 5 or at least 10 progeny.

In some embodiments the number of sequence reads in the set of sequencereads is greater than the number of unique tagged parent polynucleotidesin the set of tagged parent polynucleotides.

In some embodiments the subset of the set of amplified progenypolynucleotides sequenced is of sufficient size so that any nucleotidesequence represented in the set of tagged parent polynucleotides at apercentage that is the same as the percentage per-base sequencing errorrate of the sequencing platform used, has at least a 50%, at least a60%, at least a 70%, at least a 80%, at least a 90% at least a 95%, atleast a 98%, at least a 99%, at least a 99.9% or at least a 99.99%chance of being represented among the set of consensus sequences.

In some embodiments the method comprises enriching the set of amplifiedprogeny polynucleotides for polynucleotides mapping to one or moreselected reference sequences by: (i) selective amplification ofsequences from initial starting genetic material converted to taggedparent polynucleotides; (ii) selective amplification of tagged parentpolynucleotides; (iii) selective sequence capture of amplified progenypolynucleotides; or (iv) selective sequence capture of initial startinggenetic material.

In some embodiments analyzing comprises normalizing a measure (e.g.,number) taken from a set of consensus sequences against a measure takenfrom a set of consensus sequences from a control sample.

In some embodiments analyzing comprises detecting mutations, raremutations, single nucleotide variants, indels, copy number variations,transversions, translocations, inversion, deletions, aneuploidy, partialaneuploidy, polyploidy, chromosomal instability, chromosomal structurealterations, gene fusions, chromosome fusions, gene truncations, geneamplification, gene duplications, chromosomal lesions, DNA lesions,abnormal changes in nucleic acid chemical modifications, abnormalchanges in epigenetic patterns, abnormal changes in nucleic acidmethylation infection or cancer.

In some embodiments the polynucleotides comprise DNA, RNA, a combinationof the two or DNA plus RNA-derived cDNA.

In some embodiments a certain subset of polynucleotides is selected foror is enriched based on polynucleotide length in base-pairs from theinitial set of polynucleotides or from the amplified polynucleotides.

In some embodiments analysis further comprises detection and monitoringof an abnormality or disease within an individual, such as, infectionand/or cancer.

In some embodiments the method is performed in combination with immunerepertoire profiling.

In some embodiments the polynucleotides are extract from the groupconsisting of blood, plasma, serum, urine, saliva, mucosal excretions,sputum, stool, and tears.

In some embodiments collapsing comprising detecting and/or correctingerrors, nicks or lesions present in the sense or anti-sense strand ofthe tagged parent polynucleotides or amplified progeny polynucleotides.

This disclosure also provides for a method comprising detecting geneticvariation in initial starting genetic material with a sensitivity of atleast 5%, at least 1%, at least 0.5%, at least 0.1% or at least 0.05%.In some embodiments the initial starting genetic material is provided inan amount less than 100 ng of nucleic acid, the genetic variation iscopy number/heterozygosity variation and detecting is performed withsub-chromosomal resolution; e.g., at least 100 megabase resolution, atleast 10 megabase resolution, at least 1 megabase resolution, at least100 kilobase resolution, at least 10 kilobase resolution or at least 1kilobase resolution. In another embodiment the method comprisesproviding a plurality of sets of tagged parent polynucleotides, whereineach set is mappable to a different reference sequence. In anotherembodiment the reference sequence is the locus of a tumor marker, andanalyzing comprises detecting the tumor marker in the set of consensussequences. In another embodiment the tumor marker is present in the setof consensus sequences at a frequency less than the error rateintroduced at the amplifying step. In another embodiment the at leastone set is a plurality of sets, and the reference sequences comprise aplurality of reference sequences, each of which is the locus of a tumormarker. In another embodiment analyzing comprises detecting copy numbervariation of consensus sequences between at least two sets of parentpolynucleotides. In another embodiment analyzing comprises detecting thepresence of sequence variations compared with the reference sequences.In another embodiment analyzing comprises detecting the presence ofsequence variations compared with the reference sequences and detectingcopy number variation of consensus sequences between at least two setsof parent polynucleotides. In another embodiment collapsing comprises:i. grouping sequences reads sequenced from amplified progenypolynucleotides into families, each family amplified from the sametagged parent polynucleotide; and ii. determining a consensus sequencebased on sequence reads in a family.

This disclosure also provides for a system comprising a computerreadable medium for performing the following steps: a. providing atleast one set of tagged parent polynucleotides, and for each set oftagged parent polynucleotides; b. amplifying the tagged parentpolynucleotides in the set to produce a corresponding set of amplifiedprogeny polynucleotides; c. sequencing a subset (including a propersubset) of the set of amplified progeny polynucleotides, to produce aset of sequencing reads; and d. collapsing the set of sequencing readsto generate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides and, optionally, e. analyzing the set of consensussequences for each set of tagged parent molecules.

This disclosure also provides a method comprising: a. providing at leastone set of tagged parent polynucleotides, and for each set of taggedparent polynucleotides; b. amplifying the tagged parent polynucleotidesin the set to produce a corresponding set of amplified progenypolynucleotides; c. sequencing a subset (including a proper subset) ofthe set of amplified progeny polynucleotides, to produce a set ofsequencing reads; d. collapsing the set of sequencing reads to generatea set of consensus sequences, each consensus sequence corresponding to aunique polynucleotide among the set of tagged parent polynucleotides;and e. filtering out from among the consensus sequences those that failto meet a quality threshold. In one embodiment the quality thresholdconsiders a number of sequence reads from amplified progenypolynucleotides collapsed into a consensus sequence. In anotherembodiment the quality threshold considers a number of sequence readsfrom amplified progeny polynucleotides collapsed into a consensussequence. This disclosure also provides a system comprising a computerreadable medium for performing the aforesaid method.

This disclosure also provides a method comprising: a. providing at leastone set of tagged parent polynucleotides, wherein each set maps to adifferent reference sequence in one or more genomes, and, for each setof tagged parent polynucleotides; i. amplifying the firstpolynucleotides to produce a set of amplified polynucleotides; ii.sequencing a subset of the set of amplified polynucleotides, to producea set of sequencing reads; and iii. collapsing the sequence reads by: 1.grouping sequences reads sequenced from amplified progenypolynucleotides into families, each family amplified from the sametagged parent polynucleotide. In one embodiment collapsing furthercomprises: 2. determining a quantitative measure of sequence reads ineach family. In another embodiment the method further comprises(including a) including a): b. determining a quantitative measure ofunique families; and c. based on (1) the quantitative measure of uniquefamilies and (2) the quantitative measure of sequence reads in eachgroup, inferring a measure of unique tagged parent polynucleotides inthe set. In another embodiment inferring is performed using statisticalor probabilistic models. In another embodiment wherein the at least oneset is a plurality of sets. In another embodiment the method furthercomprises correcting for amplification or representational bias betweenthe two sets. In another embodiment the method further comprises using acontrol or set of control samples to correct for amplification orrepresentational biases between the two sets. In another embodiment themethod further comprises determining copy number variation between thesets. In another embodiment the method further comprises (including a,b, c): d. determining a quantitative measure of polymorphic forms amongthe families; and e. based on the determined quantitative measure ofpolymorphic forms, inferring a quantitative measure of polymorphic formsin the number of inferred unique tagged parent polynucleotides. Inanother embodiment wherein polymorphic forms include but are not limitedto: substitutions, insertions, deletions, inversions, microsatellitechanges, transversions, translocations, fusions, methylation,hypermethylation, hyrdroxymethylation, acetylation, epigenetic variants,regulatory-associated variants or protein binding sites. In anotherembodiment wherein the sets derive from a common sample, the methodfurther comprising: a. inferring copy number variation for the pluralityof sets based on a comparison of the inferred number of tagged parentpolynucleotides in each set mapping to each of a plurality of referencesequences. In another embodiment the original number of polynucleotidesin each set is further inferred. This disclosure also provides a systemcomprising a computer readable medium for performing the aforesaidmethods.

This disclosure also provides a method of determining copy numbervariation in a sample that includes polynucleotides, the methodcomprising: a. providing at least two sets of first polynucleotides,wherein each set maps to a different reference sequence in a genome,and, for each set of first polynucleotides; i. amplifying thepolynucleotides to produce a set of amplified polynucleotides; ii.sequencing a subset of the set of amplified polynucleotides, to producea set of sequencing reads; iii. grouping sequences reads sequenced fromamplified polynucleotides into families, each family amplified from thesame first polynucleotide in the set; iv. inferring a quantitativemeasure of families in the set; b. determining copy number variation bycomparing the quantitative measure of families in each set. Thisdisclosure also provides a system comprising a computer readable mediumfor performing the aforesaid methods.

This disclosure also provides a method of inferring frequency ofsequence calls in a sample of polynucleotides comprising: a. providingat least one set of first polynucleotides, wherein each set maps to adifferent reference sequence in one or more genomes, and, for each setof first polynucleotides; i. amplifying the first polynucleotides toproduce a set of amplified polynucleotides; ii. sequencing a subset ofthe set of amplified polynucleotides, to produce a set of sequencingreads; iii. grouping the sequence reads into families, each familycomprising sequence reads of amplified polynucleotides amplified fromthe same first polynucleotide; b. inferring, for each set of firstpolynucleotides, a call frequency for one or more bases in the set offirst polynucleotides, wherein inferring comprises: i. assigning, foreach family, confidence score for each of a plurality of calls, theconfidence score taking into consideration a frequency of the call amongmembers of the family; and ii. estimating a frequency of the one or morecalls taking into consideration the confidence scores of the one or morecalls assigned to each family. This disclosure also provides a systemcomprising a computer readable medium for performing the aforesaidmethods.

This disclosure also provides a method of communicating sequenceinformation about at least one individual polynucleotide moleculecomprising: a. providing at least one individual polynucleotidemolecule; b. encoding sequence information in the at least oneindividual polynucleotide molecule to produce a signal; c. passing atleast part of the signal through a channel to produce a received signalcomprising nucleotide sequence information about the at least oneindividual polynucleotide molecule, wherein the received signalcomprises noise and/or distortion; d. decoding the received signal toproduce a message comprising sequence information about the at least oneindividual polynucleotide molecule, wherein decoding reduces noiseand/or distortion in the message; and e. providing the message to arecipient. In one embodiment the noise comprises incorrect nucleotidecalls. In another embodiment distortion comprises uneven amplificationof the individual polynucleotide molecule compared with other individualpolynucleotide molecules. In another embodiment distortion results fromamplification or sequencing bias. In another embodiment the at least oneindividual polynucleotide molecule is a plurality of individualpolynucleotide molecules, and decoding produces a message about eachmolecule in the plurality. In another embodiment encoding comprisesamplifying the at least individual polynucleotide molecule which hasoptionally been tagged, wherein the signal comprises a collection ofamplified molecules. In another embodiment the channel comprises apolynucleotide sequencer and the received signal comprises sequencereads of a plurality of polynucleotides amplified from the at least oneindividual polynucleotide molecule. In another embodiment decodingcomprises grouping sequence reads of amplified molecules amplified fromeach of the at least one individual polynucleotide molecules. In anotherembodiment the decoding consists of a probabilistic or statisticalmethod of filtering the generated sequence signal. This disclosure alsoprovides a system comprising a computer readable medium for performingthe aforesaid methods.

In another embodiment the polynucleotides are derived from tumor genomicDNA or RNA. In another embodiment the polynucleotides are derived fromcell-free polynucleotides, exosomal polynucleotides, bacterialpolynucleotides or viral polynucleotides. In another embodiment furthercomprising the detection and/or association of affected molecularpathways. In another embodiment further comprising serial monitoring ofthe health or disease state of an individual. In another embodimentwhereby the phylogeny of a genome associated with a disease within anindividual is inferred. In another embodiment further comprisingdiagnosis, monitoring or treatment of a disease. In another embodimentthe treatment regimen is selected or modified based on detectedpolymorphic forms or CNVs or associated pathways. In another embodimentthe treatment comprises of a combination therapy.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: selecting predefined regions in a genome;accessing sequence reads and enumerating number of sequence reads in thepredefined regions; normalizing the number of sequence reads across thepredefined regions; and determining percent of copy number variation inthe predefined regions.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads; b. filtering out reads that fail to meeta set threshold; c. mapping sequence reads derived from the sequencingonto a reference sequence; d. identifying a subset of mapped sequencereads that align with a variant of the reference sequence at eachmappable base position; e. for each mappable base position, calculatinga ratio of (a) a number of mapped sequence reads that include a variantas compared to the reference sequence, to (b) a number of total sequencereads for each mappable base position; f. normalizing the ratios orfrequency of variance for each mappable base position and determiningpotential rare variant(s) or other genetic alteration(s); and g.comparing the resulting number for each of the regions with potentialrare variant(s) or mutation(s) to similarly derived numbers from areference sample.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides; b. collapsing the set of sequencing reads togenerate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides; b. collapsing the set of sequencing reads togenerate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides; c. filtering out from among the consensus sequencesthose that fail to meet a quality threshold.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides; and i. collapsing the sequence reads by: 1.grouping sequences reads sequenced from amplified progenypolynucleotides into families, each family amplified from the sametagged parent polynucleotide and, optionally, 2. determining aquantitative measure of sequence reads in each family. In certainembodiments, the executable code further performs the steps of: b.determining a quantitative measure of unique families; c. based on (1)the quantitative measure of unique families and (2) the quantitativemeasure of sequence reads in each group, inferring a measure of uniquetagged parent polynucleotides in the set. In certain embodiments, theexecutable code further performs the steps of: d. determining aquantitative measure of polymorphic forms among the families; and e.based on the determined quantitative measure of polymorphic forms,inferring a quantitative measure of polymorphic forms in the number ofinferred unique tagged parent polynucleotides.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides grouping sequences reads sequenced from amplifiedpolynucleotides into families, each family amplified from the same firstpolynucleotide in the set; b. inferring a quantitative measure offamilies in the set; c. determining copy number variation by comparingthe quantitative measure of families in each set.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides grouping the sequence reads into families, eachfamily comprising sequence reads of amplified polynucleotides amplifiedfrom the same first polynucleotide; b. inferring, for each set of firstpolynucleotides, a call frequency for one or more bases in the set offirst polynucleotides, wherein inferring comprises: c. assigning, foreach family, confidence score for each of a plurality of calls, theconfidence score taking into consideration a frequency of the call amongmembers of the family; and d. estimating a frequency of the one or morecalls taking into consideration the confidence scores of the one or morecalls assigned to each family.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data accessing a data filecomprising a received signal that comprises endoded sequence informationfrom at least one individual polynucleotide molecule wherein thereceived signal comprises noise and/or distortion; b. decoding thereceived signal to produce a message comprising sequence informationabout the at least one individual polynucleotide molecule, whereindecoding reduces noise and/or distortion about each individualpolynucleotide in the message; and c. writing the message comprisingsequence information about the at least one individual polynucleotidemolecule to a computer file.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides; b. collapsing the set of sequencing reads togenerate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides; c. filtering out from among the consensus sequencesthose that fail to meet a quality threshold.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides; and b. collapsing the sequence reads by: i.grouping sequences reads sequenced from amplified progenypolynucleotides into families, each family amplified from the sametagged parent polynucleotide; and ii. optionally, determining aquantitative measure of sequence reads in each family. In certainembodiments, the executable code further performs the steps of: c.determining a quantitative measure of unique families; d. based on (1)the quantitative measure of unique families and (2) the quantitativemeasure of sequence reads in each group, inferring a measure of uniquetagged parent polynucleotides in the set. In certain embodiments, theexecutable code further performs the steps of: e. determining aquantitative measure of polymorphic forms among the families; and f.based on the determined quantitative measure of polymorphic forms,inferring a quantitative measure of polymorphic forms in the number ofinferred unique tagged parent polynucleotides. In certain embodiments,the executable code further performs the steps of: e. inferring copynumber variation for the plurality of sets based on a comparison of theinferred number of tagged parent polynucleotides in each set mapping toeach of a plurality of reference sequences.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides; b. grouping sequences reads sequenced fromamplified polynucleotides into families, each family amplified from thesame first polynucleotide in the set; c. inferring a quantitativemeasure of families in the set; d. determining copy number variation bycomparing the quantitative measure of families in each set.

This disclosure also provides a computer readable medium innon-transitory, tangible form comprising executable code configured toperform the following steps: a. accessing a data file comprising aplurality of sequencing reads, wherein the sequence reads derive from aset of progeny polynucleotides amplified from at least one set of taggedparent polynucleotides grouping the sequence reads into families, eachfamily comprising sequence reads of amplified polynucleotides amplifiedfrom the same first polynucleotide; and b. inferring, for each set offirst polynucleotides, a call frequency for one or more bases in the setof first polynucleotides, wherein inferring comprises: i. assigning, foreach family, confidence score for each of a plurality of calls, theconfidence score taking into consideration a frequency of the call amongmembers of the family; and ii. estimating a frequency of the one or morecalls taking into consideration the confidence scores of the one or morecalls assigned to each family.

This disclosure also provides a method comprising: a. providing a samplecomprising between 100 and 100,000 haploid human genome equivalents ofcell free DNA (cfDNA) polynucleotides; and b. tagging thepolynucleotides with between 2 and 1,000,000 unique identifiers. Incertain embodiments, the number of unique identifiers is at least 3, atleast 5, at least 10, at least 15 or at least 25 and at most 100, atmost 1000 or at most 10,000. In certain embodiments, the number ofunique identifiers is at most 100, at most 1000, at most 10,000, at most100,000.

This disclosure also provides a method comprising: a. providing a samplecomprising a plurality of human haploid genome equivalents of fragmentedpolynucleotides; b. determining z, wherein z is a measure of centraltendency (e.g., mean, median or mode) of expected number of duplicatepolynucleotides starting at any position in the genome, whereinduplicate polynucleotides have the same start and stop positions; and c.tagging polynucleotides in sample with n unique identifiers, wherein nis between 2 and 100,000*z, 2 and 10,000*z, 2 and 1,000*z or 2 and100*z.

This disclosure also provides a method comprising: a. providing at leastone set of tagged parent polynucleotides, and for each set of taggedparent polynucleotides; b. producing a plurality of sequence reads foreach tagged parent polynucleotide in the set to produce a set ofsequencing reads; and c. collapsing the set of sequencing reads togenerate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides.

The disclosure provides for a method for detecting copy number variationcomprising: a) sequencing extracellular polynucleotides from a bodilysample from a subject, wherein each of the extracellular polynucleotidegenerate a plurality of sequencing reads; b) filtering out reads thatfail to meet a set threshold; c) mapping the sequence reads obtainedfrom step (a), after reads are filtered out, to a reference sequence; d)quantifying or enumerating mapped reads in two or more predefinedregions of the reference sequence; and e) determining copy numbervariation in one or more of the predefined regions by: (ii) normalizingnumber of reads in the predefined regions to each other and/or thenumber of unique sequence reads in the predefined regions to oneanother; (ii) comparing the normalized numbers obtained in step (i) tonormalized numbers obtained from a control sample.

The disclosure also provides for a method for detecting a rare mutationin a cell-free or substantially cell free sample obtained from a subjectcomprising: a) sequencing extracellular polynucleotides from a bodilysample from a subject, wherein each of the extracellular polynucleotidegenerate a plurality of sequencing reads; b) performing multiplexsequencing on regions or whole-genome sequencing if enrichment is notperformed; c) filtering out reads that fail to meet a set threshold; d)mapping sequence reads derived from the sequencing onto a referencesequence; e) identifying a subset of mapped sequence reads that alignwith a variant of the reference sequence at each mappable base position;f) for each mappable base position, calculating a ratio of (a) a numberof mapped sequence reads that include a variant as compared to thereference sequence, to (b) a number of total sequence reads for eachmappable base position; g) normalizing the ratios or frequency ofvariance for each mappable base position and determining potential rarevariant(s) or mutation(s); and h)and comparing the resulting number foreach of the regions with potential rare variant(s) or mutation(s) tosimilarly derived numbers from a reference sample.

The disclosure also provides for a method of characterizing theheterogeneity of an abnormal condition in a subject, the methodcomprising generating a genetic profile of extracellular polynucleotidesin the subject, wherein the genetic profile comprises a plurality ofdata resulting from copy number variation and rare mutation analyses.

In some embodiments, the prevalence/concentration of each rare variantidentified in the subject is reported and quantified simultaneously. Insome embodiments, a confidence score, regarding theprevalence/concentrations of rare variants in the subject, is reported.

In some embodiments, the extracellular polynucleotides comprise DNA. Insome embodiments, the extracellular polynucleotides comprise RNA.

In some embodiments, the methods further comprise isolatingextracellular polynucleotides from the bodily sample. In someembodiments, the isolating comprises a method for circulating nucleicacid isolation and extraction. In some embodiments, the methods furthercomprise fragmenting said isolated extracellular polynucleotides. Insome embodiments, the bodily sample is selected from the groupconsisting of blood, plasma, serum, urine, saliva, mucosal excretions,sputum, stool and tears.

In some embodiments, the methods further comprise the step ofdetermining the percent of sequences having copy number variation orrare mutation or variant in said bodily sample. In some embodiments, thedetermining comprises calculating the percentage of predefined regionswith an amount of polynucleotides above or below a predeterminedthreshold.

In some embodiments, the subject is suspected of having an abnormalcondition. In some embodiments, the abnormal condition is selected fromthe group consisting of mutations, rare mutations, indels, copy numbervariations, transversions, translocations, inversion, deletions,aneuploidy, partial aneuploidy, polyploidy, chromosomal instability,chromosomal structure alterations, gene fusions, chromosome fusions,gene truncations, gene amplification, gene duplications, chromosomallesions, DNA lesions, abnormal changes in nucleic acid chemicalmodifications, abnormal changes in epigenetic patterns, abnormal changesin nucleic acid methylation infection and cancer.

In some embodiments, the subject is a pregnant female. In someembodiments, the copy number variation or rare mutation or geneticvariant is indicative of a fetal abnormality. In some embodiments, thefetal abnormality is selected from the group consisting of mutations,rare mutations, indels, copy number variations, transversions,translocations, inversion, deletions, aneuploidy, partial aneuploidy,polyploidy, chromosomal instability, chromosomal structure alterations,gene fusions, chromosome fusions, gene truncations, gene amplification,gene duplications, chromosomal lesions, DNA lesions, abnormal changes innucleic acid chemical modifications, abnormal changes in epigeneticpatterns, abnormal changes in nucleic acid methylation infection andcancer.

In some embodiments, the methods further comprise attaching one or morebarcodes to the extracellular polynucleotides or fragments thereof priorto sequencing. In some embodiments, each barcode attached toextracellular polynucleotides or fragments thereof prior to sequencingis unique. In some embodiments, each barcode attached to extracellularpolynucleotides or fragments thereof prior to sequencing is not unique.

In some embodiments, the methods further comprise selectively enrichingregions from the subject's genome or transcriptome prior to sequencing.In some embodiments, the methods further comprise non-selectivelyenriching regions from the subject's genome or transcriptome prior tosequencing.

In some embodiments, the methods further comprise attaching one or morebarcodes to the extracellular polynucleotides or fragments thereof priorto any amplification or enrichment step. In some embodiments, thebarcode is a polynucleotide. In some embodiments, the barcode comprisesrandom sequence. In some embodiments, the barcode comprises a fixed orsemi-random set of oligonucleotides that in combination with thediversity of molecules sequenced from a select region enablesidentification of unique molecules. In some embodiments, the barcodescomprise oligonucleotides is at least a 3, 5, 10, 15, 20 25, 30, 35, 40,45, or 50 mer base pairs in length.

In some embodiments, the methods further comprise amplifying theextracellular polynucleotides or fragments thereof. In some embodiments,the amplification comprises global amplification or whole genomeamplification. In some embodiments, the amplification comprisesselective amplification. In some embodiments, the amplificationcomprises non-selective amplification. In some embodiments, suppressionamplification or subtractive enrichment is performed.

In some embodiments, sequence reads of unique identity are detectedbased on sequence information at the beginning (start) and end (stop)regions of the sequence read and the length of the sequence read. Insome embodiments, sequence molecules of unique identity are detectedbased on sequence information at the beginning (start) and end (stop)regions of the sequence read, the length of the sequence read andattachment of a barcode.

In some embodiments, the methods further comprise removing a subset ofthe reads from further analysis prior to quantifying or enumeratingreads. In some embodiments, removing comprises filtering out reads withan accuracy or quality score of less than a threshold, e.g., 90%, 99%,99.9%, or 99.99% and/or mapping score less than a threshold, e.g., 90%,99%, 99.9% or 99.99%. In some embodiments, the methods further comprisefiltering reads with a quality score lower than a set threshold.

In some embodiments, the predefined regions are uniform or substantiallyuniform in size. In some embodiments, the predefined regions are atleast about 10 kb, 20 kb, 30 kb 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90kb, or 100 kb in size.

In some embodiments, at least 50, 100, 200, 500, 1000, 2000, 5000,10,000, 20,000, or 50,000 regions are analyzed.

In some embodiments, the variant occurs in a region of the genomeselected from the group consisting of gene fusions, gene duplications,gene deletions, gene translocations, microsatellite regions, genefragments or combination thereof. In some embodiments, the variantoccurs in a region of the genome selected from the group consisting ofgenes, oncogenes, tumor suppressor genes, promoters, regulatory sequenceelements, or combination thereof. In some embodiments, the variant is anucleotide variant, single base substitution, small indel, transversion,translocation, inversion, deletion, truncation or gene truncation of 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 nucleotides in length.

In some embodiments, the methods further comprisecorrecting/normalizing/adjusting the quantity of mapped reads using thebarcodes or unique properties of individual reads. In some embodiments,enumerating the reads is performed through enumeration of uniquebarcodes in each of the predefined regions and normalizing those numbersacross at least a subset of predefined regions that were sequenced.

In some embodiments, samples at succeeding time intervals from the samesubject are analyzed and compared to previous sample results. In someembodiments, the method further comprises amplifying thebarcode-attached extracellular polynucleotides. In some embodiments, themethods further comprise determining partial copy number variationfrequency, determining loss of heterozygosity, performing geneexpression analysis, performing epigenetic analysis and/or performinghypermethylation analysis.

The disclosure also provides for a method comprising determining copynumber variation or performing rare mutation analysis in a cell-free orsubstantially cell free sample obtained from a subject using multiplexsequencing.

In some embodiments, the multiplex sequencing comprises performing over10,000 sequencing reactions. In some embodiments, the multiplexsequencing comprises simultaneously sequencing at least 10,000 differentreads. In some embodiments, the multiplex sequencing comprisingperforming data analysis on at least 10,000 different reads across thegenome. In some embodiments, the normalizing and detection is performedusing one or more of hidden markov, dynamic programming, support vectormachine, Bayesian or probabilistic modeling, trellis decoding, Viterbidecoding, expectation maximization, Kalman filtering, or neural networkmethodologies. In some embodiments, the methods further comprisemonitoring disease progression, monitoring residual disease, monitoringtherapy, diagnosing a condition, prognosing a condition, or selecting atherapy based on discovered variants for the subject. In someembodiments, a therapy is modified based on the most recent sampleanalysis. In some embodiments, the genetic profile of a tumor, infectionor other tissue abnormality is inferred.

In some embodiments, the growth, remission or evolution of a tumor,infection or other tissue abnormality is monitored. In some embodiments,sequences related to the subject's immune system are analyzed andmonitored at single instances or over time. In some embodiments,identification of a variant is followed up through an imaging test(e.g., CT, PET-CT, MRI, X-ray, ultrasound) for localization of thetissue abnormality suspected of causing the identified variant. In someembodiments, the analysis further comprises use of genetic data obtainedfrom a tissue or tumor biopsy from the same patient. In someembodiments, the phylogenetics of a tumor, infection or other tissueabnormality is inferred. In some embodiments, the method furthercomprises performing population-based no-calling and identification oflow-confidence regions. In some embodiments, obtaining the measurementdata for the sequence coverage comprises measuring sequence coveragedepth at every position of the genome. In some embodiments, correctingthe measurement data for the sequence coverage bias comprisescalculating window-averaged coverage. In some embodiments, correctingthe measurement data for the sequence coverage bias comprises performingadjustments to account for GC bias in the library construction andsequencing process. In some embodiments, correcting the measurement datafor the sequence coverage bias comprises performing adjustments based onadditional weighting factor associated with individual mappings tocompensate for bias.

In some embodiments, extracellular polynucleotide is derived from adiseased cell origin. In some embodiments, extracellular polynucleotideis derived from a healthy cell origin.

The disclosure also provides for a system comprising a computer readablemedium for performing the following steps: selecting predefined regionsin a genome; enumerating number of sequence reads in the predefinedregions; normalizing the number of sequence reads across the predefinedregions; and determining percent of copy number variation in thepredefined regions.

In some embodiments, the entirety of the genome or at least 85% of thegenome is analyzed. In some embodiments, the computer readable mediumprovides data on percent cancer DNA or RNA in plasma or serum to the enduser. In some embodiments, the copy number variants identified arefractional (i.e., non-integer levels) due to heterogeneity in thesample. In some embodiments, enrichment of selected regions isperformed. In some embodiments, copy number variation information issimultaneously extracted based on the methods described herein. In someembodiments, the methods comprise an initial step of polynucleotidebottlenecking to limit the number of starting initial copies ordiversity of polynucleotides in the sample.

The disclosure also provides for a method for detecting a rare mutationin a cell-free or a substantially cell free sample obtained from asubject comprising: a) sequencing extracellular polynucleotides from abodily sample of a subject, wherein each of the extracellularpolynucleotides generate a plurality of sequencing reads; b) filteringout reads that fail to meet a set quality threshold; c) mapping sequencereads derived from the sequencing onto a reference sequence; d)identifying a subset of mapped sequence reads that align with a variantof the reference sequence at each mappable base position; e) for eachmappable base position, calculating a ratio of (a) a number of mappedsequence reads that include a variant as compared to the referencesequence, to (b) a number of total sequence reads for each mappable baseposition; f) normalizing the ratios or frequency of variance for eachmappable base position and determining potential rare variant(s) orother genetic alteration(s); and g) comparing the resulting number foreach of the regions with potential rare variant(s) or mutation(s) tosimilarly derived numbers from a reference sample.

The disclosure also provides for a method comprising: a) providing atleast one set of tagged parent polynucleotides, and for each set oftagged parent polynucleotides; b) amplifying the tagged parentpolynucleotides in the set to produce a corresponding set of amplifiedprogeny polynucleotides; c) sequencing a subset (including a propersubset) of the set of amplified progeny polynucleotides, to produce aset of sequencing reads; and d) collapsing the set of sequencing readsto generate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides.

In some embodiments, each polynucleotide in a set is mappable to areference sequence. In some embodiments, the methods comprise providinga plurality of sets of tagged parent polynucleotides, wherein each setis mappable to a different mappable position in the reference sequence.In some embodiments, the method further comprises: e) analyzing the setof consensus sequences for each set of tagged parent moleculesseparately or in combination. In some embodiments, the method furthercomprises converting initial starting genetic material into the taggedparent polynucleotides. In some embodiments, the initial startinggenetic material comprises no more than 100 ng of polynucleotides. Insome embodiments, the method comprises bottlenecking the initialstarting genetic material prior to converting. In some embodiments, themethod comprises converting the initial starting genetic material intotagged parent polynucleotides with a conversion efficiency of at least10%, at least 20%, at least 30%, at least 40%, at least 50%, at least60%, at least 80% or at least 90%. In some embodiments, the convertingcomprises any of blunt-end ligation, sticky end ligation, molecularinversion probes, PCR, ligation-based PCR, single strand ligation andsingle strand circularization. In some embodiments, the initial startinggenetic material is cell-free nucleic acid. In some embodiments, aplurality of the sets map to different mappable positions in a referencesequence from the same genome.

In some embodiments, each tagged parent polynucleotide in the set isuniquely tagged. In some embodiments, each set of parent polynucleotidesis mappable to a position in a reference sequence, and thepolynucleotides in each set are not uniquely tagged. In someembodiments, the generation of consensus sequences is based oninformation from the tag and/or at least one of (i) sequence informationat the beginning (start) region of the sequence read, (ii) the end(stop) regions of the sequence read and (iii) the length of the sequenceread.

In some embodiments, the method comprises sequencing a subset of the setof amplified progeny polynucleotides sufficient to produce sequencereads for at least one progeny from of each of at least 20%, at least30%, at least 40%, at least 50%, at least 60%, at least 70%, at least80%, at least 90% at least 95%, at least 98%, at least 99%, at least99.9% or at least 99.99% of unique polynucleotides in the set of taggedparent polynucleotides. In some embodiments, the at least one progeny isa plurality of progeny, e.g., at least 2, at least 5 or at least 10progeny. In some embodiments, the number of sequence reads in the set ofsequence reads is greater than the number of unique tagged parentpolynucleotides in the set of tagged parent polynucleotides. In someembodiments, the subset of the set of amplified progeny polynucleotidessequenced is of sufficient size so that any nucleotide sequencerepresented in the set of tagged parent polynucleotides at a percentagethat is the same as the percentage per-base sequencing error rate of thesequencing platform used, has at least a 50%, at least a 60%, at least a70%, at least a 80%, at least a 90% at least a 95%, at least a 98%, atleast a 99%, at least a 99.9% or at least a 99.99% chance of beingrepresented among the set of consensus sequences.

In some embodiments, the method comprises enriching the set of amplifiedprogeny polynucleotides for polynucleotides mapping to one or moreselected mappable positions in a reference sequence by: (i) selectiveamplification of sequences from initial starting genetic materialconverted to tagged parent polynucleotides; (ii) selective amplificationof tagged parent polynucleotides; (iii) selective sequence capture ofamplified progeny polynucleotides; or (iv) selective sequence capture ofinitial starting genetic material.

In some embodiments, analyzing comprises normalizing a measure (e.g.,number) taken from a set of consensus sequences against a measure takenfrom a set of consensus sequences from a control sample. In someembodiments, analyzing comprises detecting mutations, rare mutations,indels, copy number variations, transversions, translocations,inversion, deletions, aneuploidy, partial aneuploidy, polyploidy,chromosomal instability, chromosomal structure alterations, genefusions, chromosome fusions, gene truncations, gene amplification, geneduplications, chromosomal lesions, DNA lesions, abnormal changes innucleic acid chemical modifications, abnormal changes in epigeneticpatterns, abnormal changes in nucleic acid methylation infection orcancer.

In some embodiments, the polynucleotides comprise DNA, RNA, acombination of the two, or DNA plus RNA-derived cDNA. In someembodiments, a certain subset of polynucleotides is selected for, or isenriched based on, polynucleotide length in base-pairs from the initialset of polynucleotides or from the amplified polynucleotides. In someembodiments, analysis further comprises detection and monitoring of anabnormality or disease within an individual, such as, infection and/orcancer. In some embodiments, the method is performed in combination withimmune repertoire profiling. In some embodiments, the polynucleotidesare extracted from a sample selected from the group consisting of blood,plasma, serum, urine, saliva, mucosal excretions, sputum, stool, andtears. In some embodiments, collapsing comprises detecting and/orcorrecting errors, nicks or lesions present in the sense or anti-sensestrand of the tagged parent polynucleotides or amplified progenypolynucleotides.

The disclosure also provides for a method comprising detecting geneticvariation in non-uniquely tagged initial starting genetic material witha sensitivity of at least 5%, at least 1%, at least 0.5%, at least 0.1%or at least 0.05%.

In some embodiments, the initial starting genetic material is providedin an amount less than 100 ng of nucleic acid, the genetic variation iscopy number/heterozygosity variation and detecting is performed withsub-chromosomal resolution; e.g., at least 100 megabase resolution, atleast 10 megabase resolution, at least 1 megabase resolution, at least100 kilobase resolution, at least 10 kilobase resolution or at least 1kilobase resolution. In some embodiments, the method comprises providinga plurality of sets of tagged parent polynucleotides, wherein each setis mappable to a different mappable position in a reference sequence. Insome embodiments, the mappable position in the reference sequence is thelocus of a tumor marker and analyzing comprises detecting the tumormarker in the set of consensus sequences.

In some embodiments, the tumor marker is present in the set of consensussequences at a frequency less than the error rate introduced at theamplifying step. In some embodiments, the at least one set is aplurality of sets, and the mappable position of the reference sequencecomprise a plurality of mappable positions in the reference sequence,each of which mappable position is the locus of a tumor marker. In someembodiments, analyzing comprises detecting copy number variation ofconsensus sequences between at least two sets of parent polynucleotides.In some embodiments, analyzing comprises detecting the presence ofsequence variations compared with the reference sequences.

In some embodiments, analyzing comprises detecting the presence ofsequence variations compared with the reference sequences and detectingcopy number variation of consensus sequences between at least two setsof parent polynucleotides. In some embodiments, collapsing comprises:(i) grouping sequences reads sequenced from amplified progenypolynucleotides into families, each family amplified from the sametagged parent polynucleotide; and (ii) determining a consensus sequencebased on sequence reads in a family.

The disclosure also provides for a system comprising a computer readablemedium for performing the following steps: a) accepting at least one setof tagged parent polynucleotides, and for each set of tagged parentpolynucleotides; b) amplifying the tagged parent polynucleotides in theset to produce a corresponding set of amplified progeny polynucleotides;c) sequencing a subset (including a proper subset) of the set ofamplified progeny polynucleotides, to produce a set of sequencing reads;d) collapsing the set of sequencing reads to generate a set of consensussequences, each consensus sequence corresponding to a uniquepolynucleotide among the set of tagged parent polynucleotides and,optionally, e) analyzing the set of consensus sequences for each set oftagged parent molecules.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 10% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 20% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 30% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 40% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 50% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 60% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 70% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 80% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration or amount of genetic variationin an individual, wherein the detecting is performed with the aid ofsequencing of cell-free nucleic acid, wherein at least 90% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 10% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 20% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 30% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 40% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 50% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 60% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 70% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 80% of theindividual's genome is sequenced.

The disclosure also provides for a method comprising detecting thepresence or absence of genetic alteration and amount of geneticvariation in an individual, wherein the detecting is performed with theaid of sequencing of cell-free nucleic acid, wherein at least 90% of theindividual's genome is sequenced.

In some embodiments, the genetic alteration is copy number variation orone or more rare mutations. In some embodiments, the genetic variationcomprises one or more causal variants and one or more polymorphisms. Insome embodiments, the genetic alteration and/or amount of geneticvariation in the individual may be compared to a genetic alterationand/or amount of genetic variation in one or more individuals with aknown disease. In some embodiments, the genetic alteration and/or amountof genetic variation in the individual may be compared to a geneticalteration and/or amount of genetic variation in one or moreindividuals, without a disease. In some embodiments, the cell-freenucleic acid is DNA. In some embodiments, the cell-free nucleic acid isRNA. In some embodiments, the cell-free nucleic acid is DNA and RNA. Insome embodiments, the disease is cancer or pre-cancer. In someembodiments, the method further comprising diagnosis or treatment of adisease.

The disclosure also provides for a method comprising: a) providing atleast one set of tagged parent polynucleotides, and for each set oftagged parent polynucleotides; b) amplifying the tagged parentpolynucleotides in the set to produce a corresponding set of amplifiedprogeny polynucleotides; c) sequencing a subset (including a propersubset) of the set of amplified progeny polynucleotides, to produce aset of sequencing reads; d) collapsing the set of sequencing reads togenerate a set of consensus sequences, each consensus sequencecorresponding to a unique polynucleotide among the set of tagged parentpolynucleotides; and e) filtering out from among the consensus sequencesthose that fail to meet a quality threshold.

In some embodiments, the quality threshold considers a number ofsequence reads from amplified progeny polynucleotides collapsed into aconsensus sequence. In some embodiments, the quality threshold considersa number of sequence reads from amplified progeny polynucleotidescollapsed into a consensus sequence.

The disclosure also provides for a system comprising a computer readablemedium for performing the methods described herein.

The disclosure also provides for a method comprising: a) providing atleast one set of tagged parent polynucleotides, wherein each set maps toa different mappable position in a reference sequence in one or moregenomes, and, for each set of tagged parent polynucleotides; i)amplifying the first polynucleotides to produce a set of amplifiedpolynucleotides; ii) sequencing a subset of the set of amplifiedpolynucleotides, to produce a set of sequencing reads; and iii)collapsing the sequence reads by: (1)grouping sequences reads sequencedfrom amplified progeny polynucleotides into families, each familyamplified from the same tagged parent polynucleotide.

In some embodiments, collapsing further comprises determining aquantitative measure of sequence reads in each family. In someembodiments, the method further comprises: a) determining a quantitativemeasure of unique families; and b) based on (1) the quantitative measureof unique families and (2) the quantitative measure of sequence reads ineach group, inferring a measure of unique tagged parent polynucleotidesin the set. In some embodiments, inferring is performed usingstatistical or probabilistic models. In some embodiments, the at leastone set is a plurality of sets. In some embodiments, the method furthercomprises correcting for amplification or representational bias betweenthe two sets. In some embodiments, the method further comprises using acontrol or set of control samples to correct for amplification orrepresentational biases between the two sets. In some embodiments, themethod further comprises determining copy number variation between thesets.

In some embodiments, the method further comprises: d) determining aquantitative measure of polymorphic forms among the families; and e)based on the determined quantitative measure of polymorphic forms,inferring a quantitative measure of polymorphic forms in the number ofinferred unique tagged parent polynucleotides. In some embodiments,polymorphic forms include but are not limited to: substitutions,insertions, deletions, inversions, microsatellite changes,transversions, translocations, fusions, methylation, hypermethylation,hyrdroxymethylation, acetylation, epigenetic variants,regulatory-associated variants or protein binding sites.

In some embodiments, the sets derive from a common sample, and themethod further comprises: d) inferring copy number variation for theplurality of sets based on a comparison of the inferred number of taggedparent polynucleotides in each set mapping to each of a plurality ofmappable positions in a reference sequence. In some embodiments, theoriginal number of polynucleotides in each set is further inferred. Insome embodiments, at least a subset of the tagged parent polynucleotidesin each set are non-uniquely tagged.

The disclosure also provides for a method of determining copy numbervariation in a sample that includes polynucleotides, the methodcomprising: a) providing at least two sets of first polynucleotides,wherein each set maps to a different mappable position in a referencesequence in a genome, and, for each set of first polynucleotides; (i)amplifying the polynucleotides to produce a set of amplifiedpolynucleotides; (ii) sequencing a subset of the set of amplifiedpolynucleotides, to produce a set of sequencing reads; (iii) groupingsequences reads sequenced from amplified polynucleotides into families,each family amplified from the same first polynucleotide in the set;(iv) inferring a quantitative measure of families in the set; and b)determining copy number variation by comparing the quantitative measureof families in each set.

The disclosure also provides for a method of inferring frequency ofsequence calls in a sample of polynucleotides comprising: a) providingat least one set of first polynucleotides, wherein each set maps to adifferent mappable position in a reference sequence in one or moregenomes, and, for each set of first polynucleotides; (i) amplifying thefirst polynucleotides to produce a set of amplified polynucleotides;(ii) sequencing a subset of the set of amplified polynucleotides, toproduce a set of sequencing reads; (iii) grouping the sequence readsinto families, each family comprising sequence reads of amplifiedpolynucleotides amplified from the same first polynucleotide; b)inferring, for each set of first polynucleotides, a call frequency forone or more bases in the set of first polynucleotides, wherein inferringcomprises: (i) assigning, for each family, confidence score for each ofa plurality of calls, the confidence score taking into consideration afrequency of the call among members of the family; and (ii) estimating afrequency of the one or more calls taking into consideration theconfidence scores of the one or more calls assigned to each family.

The disclosure also provides for a method of communicating sequenceinformation about at least one individual polynucleotide molecule,comprising: a) providing at least one individual polynucleotidemolecule; b) encoding sequence information in the at least oneindividual polynucleotide molecule to produce a signal; c) passing atleast part of the signal through a channel to produce a received signalcomprising nucleotide sequence information about the at least oneindividual polynucleotide molecule, wherein the received signalcomprises noise and/or distortion; d) decoding the received signal toproduce a message comprising sequence information about the at least oneindividual polynucleotide molecule, wherein decoding reduces noiseand/or distortion about each individual polynucleotide in the message;and e) providing the message comprising sequence information about theat least one individual polynucleotide molecule to a recipient.

In some embodiments, the noise comprises incorrect nucleotide calls. Insome embodiments, distortion comprises uneven amplification of theindividual polynucleotide molecule compared with other individualpolynucleotide molecules. In some embodiments, distortion results fromamplification or sequencing bias. In some embodiments, the at least oneindividual polynucleotide molecule is a plurality of individualpolynucleotide molecules, and decoding produces a message about eachmolecule in the plurality. In some embodiments, encoding comprisesamplifying the at least one individual polynucleotide molecule, whichhas optionally been tagged, wherein the signal comprises a collection ofamplified molecules. In some embodiments, the channel comprises apolynucleotide sequencer and the received signal comprises sequencereads of a plurality of polynucleotides amplified from the at least oneindividual polynucleotide molecule. In some embodiments, decodingcomprises grouping sequence reads of amplified molecules amplified fromeach of the at least one individual polynucleotide molecules. In someembodiments, the decoding consists of a probabilistic or statisticalmethod of filtering the generated sequence signal.

In some embodiments, the polynucleotides are derived from tumor genomicDNA or RNA. In some embodiments, the polynucleotides are derived fromcell-free polynucleotides, exosomal polynucleotides, bacterialpolynucleotides or viral polynucleotides. In some embodiments of any ofthe methods herein, the method further comprises the detection and/orassociation of affected molecular pathways. In some embodiments of anyof the methods herein, the method further comprises serial monitoring ofthe health or disease state of an individual. In some embodiments thephylogeny of a genome associated with a disease within an individual isinferred. In some embodiments, any of the methods described hereinfurther comprise diagnosis, monitoring or treatment of a disease. Insome embodiments, the treatment regimen is selected or modified based ondetected polymorphic forms or CNVs or associated pathways. In someembodiments, the treatment comprises of a combination therapy. In someembodiments, the diagnosis further comprises localizing the diseaseusing a radiographic technique, such as, a CT-Scan, PET-CT, MRI,Ultrasound, Ultraound with microbubbles, etc.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising:selecting predefined regions in a genome; accessing sequence reads andenumerating number of sequence reads in the predefined regions;normalizing the number of sequence reads across the predefined regions;and determining percent of copy number variation in the predefinedregions.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising:accessing a data file comprising a plurality of sequencing reads;filtering out reads that fail to meet a set threshold; mapping sequencereads derived from the sequencing onto a reference sequence; identifyinga subset of mapped sequence reads that align with a variant of thereference sequence at each mappable base position; for each mappablebase position, calculating a ratio of (a) a number of mapped sequencereads that include a variant as compared to the reference sequence, to(b) a number of total sequence reads for each mappable base position;normalizing the ratios or frequency of variance for each mappable baseposition and determining potential rare variant(s) or other geneticalteration(s); and comparing the resulting number for each of theregions with potential rare variant(s) or mutation(s) to similarlyderived numbers from a reference sample.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotides; and b)collapsing the set of sequencing reads to generate a set of consensussequences, each consensus sequence corresponding to a uniquepolynucleotide among the set of tagged parent polynucleotides.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotides; b)collapsing the set of sequencing reads to generate a set of consensussequences, each consensus sequence corresponding to a uniquepolynucleotide among the set of tagged parent polynucleotides; and c)filtering out from among the consensus sequences those that fail to meeta quality threshold.

A computer readable medium comprising non-transitory machine-executablecode that, upon execution by a computer processor, implements a method,the method comprising: a) accessing a data file comprising a pluralityof sequencing reads, wherein the sequence reads derive from a set ofprogeny polynucleotides amplified from at least one set of tagged parentpolynucleotides; and i) collapsing the sequence reads by: (1) groupingsequences reads sequenced from amplified progeny polynucleotides intofamilies, each family amplified from the same tagged parentpolynucleotide and, optionally, (2) determining a quantitative measureof sequence reads in each family.

In some embodiments, the executable code, upon execution by a computerprocessor, further performs the steps of: b) determining a quantitativemeasure of unique families; and c) based on (1) the quantitative measureof unique families and (2) the quantitative measure of sequence reads ineach group, inferring a measure of unique tagged parent polynucleotidesin the set.

In some embodiments, the executable code, upon execution by a computerprocessor, further performs the steps of: d) determining a quantitativemeasure of polymorphic forms among the families; and e) based on thedetermined quantitative measure of polymorphic forms, inferring aquantitative measure of polymorphic forms in the number of inferredunique tagged parent polynucleotides.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotidesgrouping sequences reads sequenced from amplified polynucleotides intofamilies, each family amplified from the same first polynucleotide inthe set; b) inferring a quantitative measure of families in the set; andc) determining copy number variation by comparing the quantitativemeasure of families in each set.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotidesgrouping the sequence reads into families, each family comprisingsequence reads of amplified polynucleotides amplified from the samefirst polynucleotide; b) inferring, for each set of firstpolynucleotides, a call frequency for one or more bases in the set offirst polynucleotides, wherein inferring comprises: c) assigning, foreach family, confidence score for each of a plurality of calls, theconfidence score taking into consideration a frequency of the call amongmembers of the family; and d) estimating a frequency of the one or morecalls taking into consideration the confidence scores of the one or morecalls assigned to each family.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a received signal that comprisesendoded sequence information from at least one individual polynucleotidemolecule wherein the received signal comprises noise and/or distortion;b) decoding the received signal to produce a message comprising sequenceinformation about the at least one individual polynucleotide molecule,wherein decoding reduces noise and/or distortion about each individualpolynucleotide in the message; and c) writing the message comprisingsequence information about the at least one individual polynucleotidemolecule to a computer file.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotides; b)collapsing the set of sequencing reads to generate a set of consensussequences, each consensus sequence corresponding to a uniquepolynucleotide among the set of tagged parent polynucleotides; and c)filtering out from among the consensus sequences those that fail to meeta quality threshold.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotides; and b)collapsing the sequence reads by: (i) grouping sequences reads sequencedfrom amplified progeny polynucleotides into families, each familyamplified from the same tagged parent polynucleotide; and (ii)optionally, determining a quantitative measure of sequence reads in eachfamily.

In some embodiments, the executable code, upon execution by a computerprocessor, further performs the steps of: d) determining a quantitativemeasure of unique families;e) based on (1) the quantitative measure ofunique families and (2) the quantitative measure of sequence reads ineach group, inferring a measure of unique tagged parent polynucleotidesin the set.

In some embodiments, the executable code, upon execution by a computerprocessor, further performs the steps of: e) determining a quantitativemeasure of polymorphic forms among the families; and f) based on thedetermined quantitative measure of polymorphic forms, inferring aquantitative measure of polymorphic forms in the number of inferredunique tagged parent polynucleotides.

In some embodiments, the the executable code, upon execution by acomputer processor, further performs the steps of: e) inferring copynumber variation for the plurality of sets based on a comparison of theinferred number of tagged parent polynucleotides in each set mapping toeach of a plurality of reference sequences.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising: a)accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotides; b)grouping sequences reads sequenced from amplified polynucleotides intofamilies, each family amplified from the same first polynucleotide inthe set; c) inferring a quantitative measure of families in the set; d)determining copy number variation by comparing the quantitative measureof families in each set.

The disclosure also provides for a computer readable medium comprisingnon-transitory machine-executable code that, upon execution by acomputer processor, implements a method, the method comprising:accessing a data file comprising a plurality of sequencing reads,wherein the sequence reads derive from a set of progeny polynucleotidesamplified from at least one set of tagged parent polynucleotidesgrouping the sequence reads into families, each family comprisingsequence reads of amplified polynucleotides amplified from the samefirst polynucleotide; and inferring, for each set of firstpolynucleotides, a call frequency for one or more bases in the set offirst polynucleotides, wherein inferring comprises: (i) assigning, foreach family, confidence score for each of a plurality of calls, theconfidence score taking into consideration a frequency of the call amongmembers of the family; and (ii) estimating a frequency of the one ormore calls taking into consideration the confidence scores of the one ormore calls assigned to each family.

The disclosure also provides for a composition comprising between 100and 100,000 human haploid genome equivalents of cfDNA polynucleotides,wherein the polynucleotides are tagged with between 2 and 1,000,000unique identifiers.

In some embodiments, the composition comprises between 1000 and 50,000haploid human genome equivalents of cfDNA polynucleotides, wherein thepolynucleotides are tagged with between 2 and 1,000 unique identifiers.In some embodiments, the unique identifiers comprise nucleotidebarcodes. The disclosure also provides for a method comprising: a)providing a sample comprising between 100 and 100,000 haploid humangenome equivalents of cfDNA polynucleotides; and b) tagging thepolynucleotides with between 2 and 1,000,000 unique identifiers.

The disclosure also provides for a method comprising: a) providing asample comprising a plurality of human haploid genome equivalents offragmented polynucleotides; b) determining z, wherein z is a measure ofcentral tendency (e.g., mean, median or mode) of expected number ofduplicate polynucleotides starting at any position in the genome,wherein duplicate polynucleotides have the same start and stoppositions; and c) tagging polynucleotides in sample with n uniqueidentifiers, wherein n is between 2 and 100,000*z, 2 and 10,000*z, 2 and1,000*z or 2 and 100*z. The disclosure also provides for a methodcomprising: a) providing at least one set of tagged parentpolynucleotides, and for each set of tagged parent polynucleotides; b)producing a plurality of sequence reads for each tagged parentpolynucleotide in the set to produce a set of sequencing reads; and c)collapsing the set of sequencing reads to generate a set of consensussequences, each consensus sequence corresponding to a uniquepolynucleotide among the set of tagged parent polynucleotides.

The disclosure also provides for a system comprising a computer readablemedium comprising machine-executable code as described herein. Thedisclosure also provides for a system comprising a computer readablemedium comprising machine-executable code that, upon execution by acomputer processor, implements a method as described herein.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of a system and methods of this disclosure are setforth with particularity in the appended claims. A better understandingof the features and advantages of this disclosure will be obtained byreference to the following detailed description that sets forthillustrative embodiments, in which the principles of a systems andmethods of this disclosure are utilized, and the accompanying drawingsof which:

FIG. 1 is a flow chart representation of a method of detection of copynumber variation using a single sample.

FIG. 2 is a flow chart representation of a method of detection of copynumber variation using paired samples.

FIG. 3 is a flow chart representation of a method of detection of raremutations (e.g., single nucleotide variants).

FIG. 4A is graphical copy number variation detection report generatedfrom a normal, non cancerous subject.

FIG. 4B is a graphical copy number variation detection report generatedfrom a subject with prostate cancer.

FIG. 4C is schematic representation of internet enabled access ofreports generated from copy number variation analysis of a subject withprostate cancer.

FIG. 5A is a graphical copy number variation detection report generatedfrom a subject with prostate cancer remission.

FIG. 5B is a graphical copy number variation detection report generatedfrom a subject with prostate recurrence cancer.

FIG. 6A is graphical detection report (e.g., for single nucleotidevariants) generated from various mixing experiments using DNA samplescontaining both wildtype and mutant copies of MET and TP53.

FIG. 6B is logarithmic graphical representation of (e.g., singlenucleotide variant) detection results. Observed vs. expected percentcancer measurements are shown for various mixing experiments using DNAssamples containing both wildtype and mutant copies of MET, HRAS andTP53.

FIG. 7A is graphical report of percentage of two (e.g., singlenucleotide variants) in two genes, PIK3CA and TP53, in a subject withprostate cancer as compared to a reference (control).

FIG. 7B is schematic representation of internet enabled access ofreports generated from (e.g., single nucleotide variant) analysis of asubject with prostate cancer.

FIG. 8 is a flow chart representation of a method of analyzing geneticmaterial.

FIG. 9 is a flow chart representation of a method of decodinginformation in a set of sequence reads to produce, with reduced noiseand/or distortion, a representation of information in a set of taggedparent polynucleotides.

FIG. 10 is a flow chart representation of a method of reducingdistortion in the determination of CNV from a set of sequence reads.

FIG. 11 is a flow chart representation of a method of estimatingfrequency of a base or sequence of bases at a locus in a tagged parentpolynucleotide population from a set of sequence reads.

FIG. 12 shows a method of communicating sequence information.

FIG. 13 shows detected minor allele frequencies across an entire 70 kbpanel in 0.3% LNCaP cfDNA titration using standard sequencing andDigital Sequencing workflows. Standard “analog” sequencing (FIG. 13A)masks all true-positive rare variants in tremendous noise due to PCR andsequencing errors despite Q30 filtering. Digital Sequencing (FIG. 13B)eliminates all PCR and sequencing noise, revealing true mutations withno false positives: green circles are SNP points in normal cfDNA and redcircles are detected LNCaP mutations.

FIG. 14: Shows titration of LNCap cfDNA.

FIG. 15 shows a computer system that is programmed or otherwiseconfigured to implement various methods of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION I. General Overview

The present disclosure provides a system and method for the detection ofrare mutations (e.g., single or multiple nucleotide variations) and copynumber variations in cell free polynucleotides. Generally, the systemsand methods comprise sample preparation, or the extraction and isolationof cell free polynucleotide sequences from a bodily fluid; subsequentsequencing of cell free polynucleotides by techniques known in the art;and application of bioinformatics tools to detect rare mutations andcopy number variations as compared to a reference. The systems andmethods also may contain a database or collection of different raremutations or copy number variation profiles of different diseases, to beused as additional references in aiding detection of rare mutations(e.g., single nucleotide variation profiling), copy number variationprofiling or general genetic profiling of a disease.

The systems and methods may be particularly useful in the analysis ofcell free DNAs. In some cases, cell free DNA are extracted and isolatedfrom a readily accessible bodily fluid such as blood. For example, cellfree DNA can be extracted using a variety of methods known in the art,including but not limited to isopropanol precipitation and/or silicabased purification. Cell free DNA may be extracted from any number ofsubjects, such as subjects without cancer, subjects at risk for cancer,or subjects known to have cancer (e.g. through other means).

Following the isolation/extraction step, any of a number of differentsequencing operations may be performed on the cell free polynucleotidesample. Samples may be processed before sequencing with one or morereagents (e.g., enzymes, unique identifiers (e.g., barcodes), probes,etc.). In some cases if the sample is processed with a unique identifiersuch as a barcode, the samples or fragments of samples may be taggedindividually or in subgroups with the unique identifier. The taggedsample may then be used in a downstream application such as a sequencingreaction by which individual molecules may be tracked to parentmolecules.

After sequencing data of cell free polynucleotide sequences iscollected, one or more bioinformatics processes may be applied to thesequence data to detect genetic features or aberrations such as copynumber variation, rare mutations (e.g., single or multiple nucleotidevariations) or changes in epigenetic markers, including but not limitedto methylation profiles. In some cases, in which copy number variationanalysis is desired, sequence data may be: 1) aligned with a referencegenome; 2) filtered and mapped; 3) partitioned into windows or bins ofsequence; 4) coverage reads counted for each window; 5) coverage readscan then be normalized using a stochastic or statistical modelingalgorithm; 6) and an output file can be generated reflecting discretecopy number states at various positions in the genome. In other cases,in which rare mutation analysis is desired, sequence data may be 1)aligned with a reference genome; 2) filtered and mapped; 3) frequency ofvariant bases calculated based on coverage reads for that specific base;4) variant base frequency normalized using a stochastic, statistical orprobabilistic modeling algorithm; 5) and an output file can be generatedreflecting mutation states at various positions in the genome.

A variety of different reactions and/operations may occur within thesystems and methods disclosed herein, including but not limited to:nucleic acid sequencing, nucleic acid quantification, sequencingoptimization, detecting gene expression, quantifying gene expression,genomic profiling, cancer profiling, or analysis of expressed markers.Moreover, the systems and methods have numerous medical applications.For example, it may be used for the identification, detection,diagnosis, treatment, staging of, or risk prediction of various geneticand non-genetic diseases and disorders including cancer. It may be usedto assess subject response to different treatments of said genetic andnon-genetic diseases, or provide information regarding diseaseprogression and prognosis.

Polynucleotide sequencing can be compared with a problem incommunication theory. An initial individual polynucleotide or ensembleof polynucleotides is thought of as an original message. Tagging and/oramplifying can be thought of as encoding the original message into asignal. Sequencing can be thought of as communication channel. Theoutput of a sequencer, e.g., sequence reads, can be thought of as areceived signal. Bioinformatic processing can be thought of as areceiver that decodes the received signal to produce a transmittedmessage, e.g., a nucleotide sequence or sequences. The received signalcan include artifacts, such as noise and distortion. Noise can bethought of as an unwanted random addition to a signal. Distortion can bethought of as an alteration in the amplitude of a signal or portion of asignal.

Noise can be introduced through errors in copying and/or reading apolynucleotide. For example, in a sequencing process a singlepolynucleotide can first be subject to amplification. Amplification canintroduce errors, so that a subset of the amplified polynucleotides maycontain, at a particular locus, a base that is not the same as theoriginal base at that locus. Furthermore, in the reading process a baseat any particular locus may be read incorrectly. As a consequence, thecollection of sequence reads can include a certain percentage of basecalls at a locus that are not the same as the original base. In typicalsequencing technologies this error rate can be in the single digits,e.g., 2%-3%. When a collection of molecules that are all presumed tohave the same sequence are sequenced, this noise is sufficiently smallthat one can identify the original base with high reliability.

However, if a collection of parent polynucleotides includes a subset ofpolynucleotides having sequence variants at a particular locus, noisecan be a significant problem. This can be the case, for example, whencell free DNA includes not only germline DNA, but DNA from anothersource, such as fetal DNA or DNA from a cancer cell. In this case, ifthe frequency of molecules with sequence variants is in the same rangeas the frequency of errors introduced by the sequencing process, thentrue sequence variants may not be distinguishable from noise. This couldinterfere, for example, with detecting sequence variants in a sample.

Distortion can be manifested in the sequencing process as a differencein signal strength, e.g., total number of sequence reads, produced bymolecules in a parent population at the same frequency. Distortion canbe introduced, for example, through amplification bias, GC bias, orsequencing bias. This could interfere with detecting copy numbervariation in a sample. GC bias results in the uneven representation ofareas rich or poor in GC content in the sequence reading.

This invention provides methods of reducing sequencing artifacts, suchas noise and/or distortion, in a polynucleotide sequencing process.Grouping sequence reads into families derived from original individualmolecules can reduce noise and/or distortion from a single individualmolecule or from an ensemble of molecules. With respect to a singlemolecule, grouping reads into a family reduces distortion by, forexample, indicating that many sequence reads actually represent a singlemolecule rather than many different molecules. Collapsing sequence readsinto a consensus sequence is one way to reduce noise in the receivedmessage from one molecule. Using probabilistic functions that convertreceived frequencies is another way. With respect to an ensemble ofmolecules, grouping reads into families and determining a quantitativemeasure of the families reduces distortion, for example, in the quantityof molecules at each of a plurality of different loci. Again, collapsingsequence reads of different families into consensus sequences eliminateerrors introduced by amplification and/or sequencing error. Furthermore,determining frequencies of base calls based on probabilities derivedfrom family information also reduces noise in the received message froman ensemble of molecules.

Methods of reducing noise and/or distortion from a sequencing processare known. These include, for example, filtering sequences, e.g.,requiring them to meet a quality threshold, or reducing GC bias. Suchmethods typically are performed on the collection of sequence reads thatare the output of a sequencer, and can be performed sequenceread-by-sequence read, without regard for family structure(sub-collections of sequences derived from a single original parentmolecule). Certain methods of this invention reduce noise and distortionby reducing noise and/or distortion within families of sequence reads,that is, operating on sequence reads grouped into families derived froma single parent polynucleotide molecule. Signal artifact reduction atthe family level can produce significantly less noise and distortion inthe ultimate message that is provided than artifact reduction performedat a sequence read-by-sequence read level or on sequencer output as awhole.

The present disclosure further provides methods and systems fordetecting with high sensitivity genetic variation in a sample of initialgenetic material. The methods involve using one or both of the followingtools: First, the efficient conversion of individual polynucleotides ina sample of initial genetic material into sequence-ready tagged parentpolynucleotides, so as to increase the probability that individualpolynucleotides in a sample of initial genetic material will berepresented in a sequence-ready sample. This can produce sequenceinformation about more polynucleotides in the initial sample. Second,high yield generation of consensus sequences for tagged parentpolynucleotides by high rate sampling of progeny polynucleotidesamplified from the tagged parent polynucleotides, and collapsing ofgenerated sequence reads into consensus sequences representing sequencesof parent tagged polynucleotides. This can reduce noise introduced byamplification bias and/or sequencing errors, and can increasesensitivity of detection. Collapsing is performed on a plurality ofsequence reads, generated either from reads of amplified molecules, ormultiple reads of a single molecule.

Sequencing methods typically involve sample preparation, sequencing ofpolynucleotides in the prepared sample to produce sequence reads andbioinformatic manipulation of the sequence reads to produce quantitativeand/or qualitative genetic information about the sample. Samplepreparation typically involves converting polynucleotides in a sampleinto a form compatible with the sequencing platform used. Thisconversion can involve tagging polynucleotides. In certain embodimentsof this invention the tags comprise polynucleotide sequence tags.Conversion methodologies used in sequencing may not be 100% efficient.For example, it is not uncommon to convert polynucleotides in a samplewith a conversion efficiency of about 1-5%, that is, about 1-5% of thepolynucleotides in a sample are converted into tagged polynucleotides.Polynucleotides that are not converted into tagged molecules are notrepresented in a tagged library for sequencing. Accordingly,polynucleotides having genetic variants represented at low frequency inthe initial genetic material may not be represented in the taggedlibrary and, therefore may not be sequenced or detected. By increasingconversion efficiency, the probability that a rare polynucleotide in theinitial genetic material will be represented in the tagged library and,consequently, detected by sequencing is increased. Furthermore, ratherthan directly address the low conversion efficiency issue of librarypreparation, most protocols to date call for greater than 1 microgram ofDNA as input material. However, when input sample material is limited ordetection of polynucleotides with low representation is desired, highconversion efficiency can efficiently sequence the sample and/or toadequately detect such polynucleotides.

This disclosure provides methods of converting initial polynucleotidesinto tagged polynucleotides with a conversion efficiency of at least10%, at least 20%, at least 30%, at least 40%, at least 50%, at least60%, at least 80% or at least 90%. The methods involve, for example,using any of blunt-end ligation, sticky end ligation, molecularinversion probes, PCR, ligation-based PCR, multiplex PCR, single strandligation and single strand circularization. The methods can also involvelimiting the amount of initial genetic material. For example, the amountof initial genetic material can be less than 1 ug, less than 100 ng orless than 10 ng. These methods are described in more detail herein.

Obtaining accurate quantitative and qualitative information aboutpolynucleotides in a tagged library can result in a more sensitivecharacterization of the initial genetic material. Typically,polynucleotides in a tagged library are amplified and the resultingamplified molecules are sequenced. Depending on the throughput of thesequencing platform used, only a subset of the molecules in theamplified library produce sequence reads. So, for example, the number ofamplified molecules sampled for sequencing may be about only 50% of theunique polynucleotides in the tagged library. Furthermore, amplificationmay be biased in favor of or against certain sequences or certainmembers of the tagged library. This may distort quantitative measurementof sequences in the tagged library. Also, sequencing platforms canintroduce errors in sequencing. For example, sequences can have aper-base error rate of 0.5-1%. Amplification bias and sequencing errorsintroduce noise into the final sequencing product. This noise candiminish sensitivity of detection. For example, sequence variants whosefrequency in the tagged population is less than the sequencing errorrate can be mistaken for noise. Also, by providing reads of sequences ingreater or less amounts than their actual number in a population,amplification bias can distort measurements of copy number variation.Alternatively, a plurality of sequence reads from a singlepolynucleotide can be produced without amplification. This can be done,for example, with nanopore methods.

This disclosure provides methods of accurately detecting and readingunique polynucleotides in a tagged pool. In certain embodiments thisdisclosure provides sequence-tagged polynucleotides that, when amplifiedand sequenced, or when sequenced a plurality of times to produce aplurality of sequence reads, provide information that allowed thetracing back, or collapsing, of progeny polynucleotides to the uniquetag parent polynucleotide molecule. Collapsing families of amplifiedprogeny polynucleotides reduces amplification bias by providinginformation about original unique parent molecules. Collapsing alsoreduces sequencing errors by eliminating from sequencing data mutantsequences of progeny molecules.

Detecting and reading unique polynucleotides in the tagged library caninvolve two strategies. In one strategy a sufficiently large subset ofthe amplified progeny polynucleotide pool is a sequenced such that, fora large percentage of unique tagged parent polynucleotides in the set oftagged parent polynucleotides, there is a sequence read that is producedfor at least one amplified progeny polynucleotide in a family producedfrom a unique tagged parent polynucleotide. In a second strategy, theamplified progeny polynucleotide set is sampled for sequencing at alevel to produce sequence reads from multiple progeny members of afamily derived from a unique parent polynucleotide. Generation ofsequence reads from multiple progeny members of a family allowscollapsing of sequences into consensus parent sequences.

So, for example, sampling a number of amplified progeny polynucleotidesfrom the set of amplified progeny polynucleotides that is equal to thenumber of unique tagged parent polynucleotides in the set of taggedparent polynucleotides (particularly when the number is at least 10,000)will produce, statistically, a sequence read for at least one of progenyof about 68% of the tagged parent polynucleotides in the set, and about40% of the unique tagged parent polynucleotides in the original set willbe represented by at least two progeny sequence reads. In certainembodiments the amplified progeny polynucleotide set is sampledsufficiently so as to produce an average of five to ten sequence readsfor each family. Sampling from the amplified progeny set of 10-times asmany molecules as the number of unique tagged parent polynucleotideswill produce, statistically, sequence information about 99.995% of thefamilies, of which 99.95% of the total families will be covered by aplurality of sequence reads. A consensus sequence can be built from theprogeny polynucleotides in each family so as to dramatically reduce theerror rate from the nominal per-base sequencing error rate to a ratepossibly many orders of magnitude lower. For example, if the sequencerhas a random per-base error rate of 1% and the chosen family has 10reads, a consensus sequence built from these 10 reads would possess anerror rate of below 0.0001%. Accordingly, the sampling size of theamplified progeny to be sequenced can be chosen so as to ensure asequence having a frequency in the sample that is no greater than thenominal per-base sequencing error rate to a rate of the sequencingplatform used, has at least 99% chance being represented by at least oneread.

In another embodiment the set of amplified progeny polynucleotides issampled to a level to produce a high probability e.g., at least 90%,that a sequence represented in the set of tagged parent polynucleotidesat a frequency that is about the same as the per base sequencing errorrate of the sequencing platform used is covered by at least one sequenceread and preferably a plurality of sequence reads. So, for example, ifthe sequencing platform has a per base error rate of 0.2% in a sequenceor set of sequences is represented in the set of tagged parentpolynucleotides at a frequency of about 0.2%, then the number ofpolynucleotides in the amplified progeny pool that are sequenced can beabout X times the number of unique molecules in the set of tagged parentpolynucleotides.

These methods can be combined with any of the noise reduction methodsdescribed. Including, for example, qualifying sequence reads forinclusion in the pool of sequences used to generate consensus sequences.

This information can now be used for both qualitative and quantitativeanalysis. For example, for quantitative analysis, a measure, e.g., acount, of the amount of tagged parent molecules mapping to a referencesequence is determined. This measure can be compared with a measure oftagged parent molecules mapping to a different genomic region. That is,the amount of tagged parent molecules mapping to a first location ormappable position in a reference sequence, such as the human genome, canbe compared with a measure of tagged parent molecules mapping to asecond location or mappable position in a reference sequence. Thiscomparison can reveal, for example, the relative amounts of parentmolecules mapping to each region. This, in turn, provides an indicationof copy number variation for molecules mapping to a particular region.For example, if the measure of polynucleotides mapping to a firstreference sequence is greater than the measure of polynucleotidesmapping to a second reference sequence, this may indicate that theparent population, and by extension the original sample, includedpolynucleotides from cells exhibiting aneuploidy. The measures can benormalized against a control sample to eliminate various biases.Quantitative measures can include, for example, number, count, frequency(whether relative, inferred or absolute).

A reference genome can include the genome of any species of interest.Human genome sequences useful as references can include the hg19assembly or any previous or available hg assembly. Such sequences can beinterrogated using the genome brower available atgenome.ucsc.edu/index.html. Other species genomes include, for examplePanTro2 (chimp) and mm9 (mouse).

For qualitative analysis, sequences from a set of tagged polynucleotidesmapping to a reference sequence can be analyzed for variant sequencesand their frequency in the population of tagged parent polynucleotidescan be measured.

II. Sample Preparation A. Polynucleotide Isolation and Extraction

The systems and methods of this disclosure may have a wide variety ofuses in the manipulation, preparation, identification and/orquantification of cell free polynucleotides. Examples of polynucleotidesinclude but are not limited to: DNA, RNA, amplicons, cDNA, dsDNA, ssDNA,plasmid DNA, cosmid DNA, high Molecular Weight (MW) DNA, chromosomalDNA, genomic DNA, viral DNA, bacterial DNA, mtDNA (mitochondrial DNA),mRNA, rRNA, tRNA, nRNA, siRNA, snRNA, snoRNA, scaRNA, microRNA, dsRNA,ribozyme, riboswitch and viral RNA (e.g., retroviral RNA).

Cell free polynucleotides may be derived from a variety of sourcesincluding human, mammal, non-human mammal, ape, monkey, chimpanzee,reptilian, amphibian, or avian, sources. Further, samples may beextracted from variety of animal fluids containing cell free sequences,including but not limited to blood, serum, plasma, vitreous, sputum,urine, tears, perspiration, saliva, semen, mucosal excretions, mucus,spinal fluid, amniotic fluid, lymph fluid and the like. Cell freepolynucleotides may be fetal in origin (via fluid taken from a pregnantsubject), or may be derived from tissue of the subject itself.

Isolation and extraction of cell free polynucleotides may be performedthrough collection of bodily fluids using a variety of techniques. Insome cases, collection may comprise aspiration of a bodily fluid from asubject using a syringe. In other cases collection may comprisepipetting or direct collection of fluid into a collecting vessel.

After collection of bodily fluid, cell free polynucleotides may beisolated and extracted using a variety of techniques known in the art.In some cases, cell free DNA may be isolated, extracted and preparedusing commercially available kits such as the Qiagen Qiamp® CirculatingNucleic Acid Kit protocol. In other examples, Qiagen Qubit™ dsDNA HSAssay kit protocol, Agilent™ DNA 1000 kit, or TruSeq™ Sequencing LibraryPreparation; Low-Throughput (LT) protocol may be used.

Generally, cell free polynucleotides are extracted and isolated by frombodily fluids through a partitioning step in which cell free DNAs, asfound in solution, are separated from cells and other non solublecomponents of the bodily fluid. Partitioning may include, but is notlimited to, techniques such as centrifugation or filtration. In othercases, cells are not partitioned from cell free DNA first, but ratherlysed. In this example, the genomic DNA of intact cells is partitionedthrough selective precipitation. Cell free polynucleotides, includingDNA, may remain soluble and may be separated from insoluble genomic DNAand extracted. Generally, after addition of buffers and other wash stepsspecific to different kits, DNA may be precipitated using isopropanolprecipitation. Further clean up steps may be used such as silica basedcolumns to remove contaminants or salts. General steps may be optimizedfor specific applications. Non specific bulk carrier polynucleotides,for example, may be added throughout the reaction to optimize certainaspects of the procedure such as yield.

Isolation and purification of cell free DNA may be accomplished usingany means, including, but not limited to, the use of commercial kits andprotocols provided by companies such as Sigma Aldrich, LifeTechnologies, Promega, Affymetrix, IBI or the like. Kits and protocolsmay also be non-commercially available.

After isolation, in some cases, the cell free polynucleotides arepre-mixed with one or more additional materials, such as one or morereagents (e.g., ligase, protease, polymerase) prior to sequencing.

One method of increasing conversion efficiency involves using a ligaseengineered for optimal reactivity on single-stranded DNA, such as aThermoPhage ssDNA ligase derivative. Such ligases bypass traditionalsteps in library preparation of end-repair and A-tailing that can havepoor efficiencies and/or accumulated losses due to intermediate cleanupsteps, and allows for twice the probability that either the sense oranti-sense starting polynucleotide will be converted into anappropriately tagged polynucleotide. It also converts double-strandedpolynucleotides that may possess overhangs that may not be sufficientlyblunt-ended by the typical end-repair reaction. Optimal reactionsconditions for this ssDNA reaction are: 1× reaction buffer (50 mM MOPS(pH 7.5), 1 mM DTT, 5 mM MgCl2, 10 mM KCl). With 50 mM ATP, 25 mg/mlBSA, 2.5 mM MnCl2 , 200 pmol 85 nt ssDNA oligomer and 5 U ssDNA ligaseincubated at 65° C. for 1 hour. Subsequent amplification using PCR canfurther convert the tagged single-stranded library to a double-strandedlibrary and yield an overall conversion efficiency of well above 20%.Other methods of increasing conversion rate, e.g., to above 10%,include, for example, any of the following, alone or in combination:Annealing-optimized molecular-inversion probes, blunt-end ligation witha well-controlled polynucleotide size range, sticky-end ligation or anupfront multiplex amplification step with or without the use of fusionprimers.

B. Molecular Bar Coding of Cell Free Polynucleotides

The systems and methods of this disclosure may also enable the cell freepolynucleotides to be tagged or tracked in order to permit subsequentidentification and origin of the particular polynucleotide. This featureis in contrast with other methods that use pooled or multiplex reactionsand that only provide measurements or analyses as an average of multiplesamples. Here, the assignment of an identifier to individual orsubgroups of polynucleotides may allow for a unique identity to beassigned to individual sequences or fragments of sequences. This mayallow acquisition of data from individual samples and is not limited toaverages of samples.

In some examples, nucleic acids or other molecules derived from a singlestrand may share a common tag or identifier and therefore may be lateridentified as being derived from that strand. Similarly, all of thefragments from a single strand of nucleic acid may be tagged with thesame identifier or tag, thereby permitting subsequent identification offragments from the parent strand. In other cases, gene expressionproducts (e.g., mRNA) may be tagged in order to quantify expression, bywhich the barcode, or the barcode in combination with sequence to whichit is attached can be counted. In still other cases, the systems andmethods can be used as a PCR amplification control. In such cases,multiple amplification products from a PCR reaction can be tagged withthe same tag or identifier. If the products are later sequenced anddemonstrate sequence differences, differences among products with thesame identifier can then be attributed to PCR error.

Additionally, individual sequences may be identified based uponcharacteristics of sequence data for the read themselves. For example,the detection of unique sequence data at the beginning (start) and end(stop) portions of individual sequencing reads may be used, alone or incombination, with the length, or number of base pairs of each sequenceread unique sequence to assign unique identities to individualmolecules. Fragments from a single strand of nucleic acid, having beenassigned a unique identity, may thereby permit subsequent identificationof fragments from the parent strand. This can be used in conjunctionwith bottlenecking the initial starting genetic material to limitdiversity.

Further, using unique sequence data at the beginning (start) and end(stop) portions of individual sequencing reads and sequencing readlength may be used, alone or combination, with the use of barcodes. Insome cases, the barcodes may be unique as described herein. In othercases, the barcodes themselves may not be unique. In this case, the useof non unique barcodes, in combination with sequence data at thebeginning (start) and end (stop) portions of individual sequencing readsand sequencing read length may allow for the assignment of a uniqueidentity to individual sequences. Similarly, fragments from a singlestrand of nucleic acid having been assigned a unique identity, maythereby permit subsequent identification of fragments from the parentstrand.

Generally, the methods and systems provided herein are useful forpreparation of cell free polynucleotide sequences to a down-streamapplication sequencing reaction. Often, a sequencing method is classicSanger sequencing. Sequencing methods may include, but are not limitedto: high-throughput sequencing, pyrosequencing, sequencing-by-synthesis,single-molecule sequencing, nanopore sequencing, semiconductorsequencing, sequencing-by-ligation, sequencing-by-hybridization, RNA-Seq(Illumina), Digital Gene Expression (Helicos), Next generationsequencing, Single Molecule Sequencing by Synthesis (SMSS)(Helicos),massively-parallel sequencing, Clonal Single Molecule Array (Solexa),shotgun sequencing, Maxim-Gilbert sequencing, primer walking, and anyother sequencing methods known in the art.

C. Assignment of Barcodes to Cell Free Polynucleotide Sequences

The systems and methods disclosed herein may be used in applicationsthat involve the assignment of unique or non-unique identifiers, ormolecular barcodes, to cell free polynucleotides. Often, the identifieris a bar-code oligonucleotide that is used to tag the polynucleotide;but, in some cases, different unique identifiers are used. For example,in some cases, the unique identifier is a hybridization probe. In othercases, the unique identifier is a dye, in which case the attachment maycomprise intercalation of the dye into the analyte molecule (such asintercalation into DNA or RNA) or binding to a probe labeled with thedye. In still other cases, the unique identifier may be a nucleic acidoligonucleotide, in which case the attachment to the polynucleotidesequences may comprise a ligation reaction between the oligonucleotideand the sequences or incorporation through PCR. In other cases, thereaction may comprise addition of a metal isotope, either directly tothe analyte or by a probe labeled with the isotope. Generally,assignment of unique or non-unique identifiers, or molecular barcodes inreactions of this disclosure may follow methods and systems describedby, for example, US patent applications 20010053519, 20030152490,20110160078 and U.S. Pat. No. 6,582,908.

Often, the method comprises attaching oligonucleotide barcodes tonucleic acid analytes through an enzymatic reaction including but notlimited to a ligation reaction. For example, the ligase enzyme maycovalently attach a DNA barcode to fragmented DNA (e.g., highmolecular-weight DNA). Following the attachment of the barcodes, themolecules may be subjected to a sequencing reaction.

However, other reactions may be used as well. For example,oligonucleotide primers containing barcode sequences may be used inamplification reactions (e.g., PCR, qPCR, reverse-transcriptase PCR,digital PCR, etc.) of the DNA template analytes, thereby producingtagged analytes. After assignment of barcodes to individual cell freepolynucleotide sequences, the pool of molecules may be sequenced.

In some cases, PCR may be used for global amplification of cell freepolynucleotide sequences. This may comprise using adapter sequences thatmay be first ligated to different molecules followed by PCRamplification using universal primers. PCR for sequencing may beperformed using any means, including but not limited to use ofcommercial kits provided by Nugen (WGA kit), Life Technologies,Affymetrix, Promega, Qiagen and the like. In other cases, only certaintarget molecules within a population of cell free polynucleotidemolecules may be amplified. Specific primers, may in conjunction withadapter ligation, may be used to selectively amplify certain targets fordownstream sequencing.

The unique identifiers (e.g., oligonucleotide bar-codes, antibodies,probes, etc.) may be introduced to cell free polynucleotide sequencesrandomly or non-randomly. In some cases, they are introduced at anexpected ratio of unique identifiers to microwells. For example, theunique identifiers may be loaded so that more than about 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000,500,000, 1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 uniqueidentifiers are loaded per genome sample. In some cases, the uniqueidentifiers may be loaded so that less than about 2, 3, 4, 5, 6, 7, 8,9, 10, 20, 50, 100, 500, 1000, 5000, 10000, 50,000, 100,000, 500,000,1,000,000, 10,000,000, 50,000,000 or 1,000,000,000 unique identifiersare loaded per genome sample. In some cases, the average number ofunique identifiers loaded per sample genome is less than, or greaterthan, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000, 5000,10000, 50,000, 100,000, 500,000, 1,000,000, 10,000,000, 50,000,000 or1,000,000,000 unique identifiers per genome sample.

In some cases, the unique identifiers may be a variety of lengths suchthat each barcode is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20,50, 100, 500, 1000 base pairs. In other cases, the barcodes may compriseless than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, 100, 500, 1000 basepairs.

In some cases, unique identifiers may be predetermined or random orsemi-random sequence oligonucleotides. In other cases, a plurality ofbarcodes may be used such that barcodes are not necessarily unique toone another in the plurality. In this example, barcodes may be ligatedto individual molecules such that the combination of the bar code andthe sequence it may be ligated to creates a unique sequence that may beindividually tracked. As described herein, detection of non uniquebarcodes in combination with sequence data of beginning (start) and end(stop) portions of sequence reads may allow assignment of a uniqueidentity to a particular molecule. The length, or number of base pairs,of an individual sequence read may also be used to assign a uniqueidentity to such a molecule. As described herein, fragments from asingle strand of nucleic acid having been assigned a unique identity,may thereby permit subsequent identification of fragments from theparent strand. In this way the polynucleotides in the sample can beuniquely or substantially uniquely tagged.

The unique identifiers may be used to tag a wide range of analytes,including but not limited to RNA or DNA molecules. For example, uniqueidentifiers (e.g., barcode oligonucleotides) may be attached to wholestrands of nucleic acids or to fragments of nucleic acids (e.g.,fragmented genomic DNA, fragmented RNA). The unique identifiers (e.g.,oligonucleotides) may also bind to gene expression products, genomicDNA, mitochondrial DNA, RNA, mRNA, and the like.

In many applications, it may be important to determine whetherindividual cell free polynucleotide sequences each receive a differentunique identifier (e.g., oligonucleotide barcode). If the population ofunique identifiers introduced into the systems and methods is notsignificantly diverse, different analytes may possibly be tagged withidentical identifiers. The systems and methods disclosed herein mayenable detection of cell free polynucleotide sequences tagged with thesame identifier. In some cases, a reference sequences may be includedwith the population of cell free polynucleotide sequences to beanalyzed. The reference sequence may be, for example, a nucleic acidwith a known sequence and a known quantity. If the unique identifiersare oligonucleotide barcodes and the analytes are nucleic acids, thetagged analytes may subsequently be sequenced and quantified. Thesemethods may indicate if one or more fragments and/or analytes may havebeen assigned an identical barcode.

A method disclosed herein may comprise utilizing reagents necessary forthe assignment of barcodes to the analytes. In the case of ligationreactions, reagents including, but not limited to, ligase enzyme,buffer, adapter oligonucleotides, a plurality of unique identifier DNAbarcodes and the like may be loaded into the systems and methods. In thecase of enrichment, reagents including but not limited to a plurality ofPCR primers, oligonucleotides containing unique identifying sequence, orbarcode sequence, DNA polymerase, DNTPs, and buffer and the like may beused in preparation for sequencing.

Generally, the method and system of this disclosure may utilize themethods of U.S. Pat. No. 7,537,897 in using molecular barcodes to countmolecules or analytes.

In a sample comprising fragmented genomic DNA, e.g., cell-free DNA(cfDNA), from a plurality of genomes, there is some likelihood that morethan one polynucleotide from different genomes will have the same startand stop positions (“duplicates” or “cognates”). The probable number ofduplicates beginning at any position is a function of the number ofhaploid genome equivalents in a sample and the distribution of fragmentsizes. For example, cfDNA has a peak of fragments at about 160nucleotides, and most of the fragments in this peak range from about 140nucleotides to 180 nucleotides. Accordingly, cfDNA from a genome ofabout 3 billion bases (e.g., the human genome) may be comprised ofalmost 20 million (2×10⁷) polynucleotide fragments. A sample of about 30ng DNA can contain about 10,000 haploid human genome equivalents.(Similarly, a sample of about 100 ng of DNA can contain about 30,000haploid human genome equivalents.) A sample containing about 10,000 (10)haploid genome equivalents of such DNA can have about 200 billion(2×10¹¹) individual polynucleotide molecules. It has been empiricallydetermined that in a sample of about 10,000 haploid genome equivalentsof human DNA, there are about 3 duplicate polynucleotides beginning atany given position. Thus, such a collection can contain a diversity ofabout 6×10¹⁰-8×10¹° (about 60 billion-80 billion e.g., about 70 billion(7×10¹⁰)) differently sequenced polynucleotide molecules.

The probability of correctly identifying molecules is dependent oninitial number of genome equivalents, the length distribution ofsequenced molecules, sequence uniformity and number of tags. When thetag count is equal to one, that is, equivalent to having no unique tagsor not tagging. The table below lists the probability of correctlyidentifying a molecule as unique assuming a typical cell-free sizedistribution as above.

Tag % Correctly uniquely Tag Count identified 1000 human haploid genomeequivalents 1 96.9643 4 99.2290 9 99.6539 16 99.8064 25 99.8741 10099.9685 3000 human haploid genome equivalents 1 91.7233 4 97.8178 999.0198 16 99.4424 25 99.6412 100 99.9107

In this case, upon sequencing the genomic DNA, it may not be possible todetermine which sequence reads are derived from which parent molecules.This problem can be diminished by tagging parent molecules with asufficient number of unique identifiers (e.g., the tag count) such thatthere is a likelihood that two duplicate molecules, i.e., moleculeshaving the same start and stop positions, bear different uniqueidentifiers so that sequence reads are traceable back to particularparent molecules. One approach to this problem is to uniquely tag every,or nearly every, different parent molecule in the sample. However,depending on the number of haploid gene equivalents and distribution offragment sizes in the sample, this may require billions of differentunique identifiers.

This method can be cumbersome and expensive. This invention providesmethods and compositions in which a population of polynucleotides in asample of fragmented genomic DNA is tagged with n different uniqueidentifiers, wherein n is at least 2 and no more than 100,000*z, whereinz is a measure of central tendency (e.g., mean, median, mode) of anexpected number of duplicate molecules having the same start and stoppositions. In certain embodiments, n is at least any of 2*z, 3*z, 4*z,5*z, 6*z, 7*z, 8*z, 9*z, 10*z, 11*z, 12*z, 13*z, 14*z, 15*z, 16*z, 17*z,18*z, 19*z, or 20*z (e.g., lower limit). In other embodiments, n is nogreater thanl00,000*z, 10,000*z, 1000*z or 100*z (e.g., upper limit).Thus, n can range between any combination of these lower and upperlimits. In certain embodiments, n is between 5*z and 15*z, between 8*zand 12*z, or about 10*z. For example, a haploid human genome equivalenthas about 3 picograms of DNA. A sample of about 1 microgram of DNAcontains about 300,000 haploid human genome equivalents. The number ncan be between 15 and 45, between 24 and 36 or about 30. Improvements insequencing can be achieved as long as at least some of the duplicate orcognate polynucleotides bear unique identifiers, that is, bear differenttags. However, in certain embodiments, the number of tags used isselected so that there is at least a 95% chance that all duplicatemolecules starting at any one position bear unique identifiers. Forexample, a sample comprising about 10,000 haploid human genomeequivalents of cfDNA can be tagged with about 36 unique identifiers. Theunique identifiers can comprise six unique DNA barcodes. Attached toboth ends of a polynucleotide, 36 possible unique identifiers areproduced. Samples tagged in such a way can be those with a range ofabout 10 ng to any of about 100 ng, about 1 μg, about 10 μg offragmented polynucleotides, e.g., genomic DNA, e.g. cfDNA.

Accordingly, this invention also provides compositions of taggedpolynucleotides. The polynucleotides can comprise fragmented DNA, e.g.cfDNA. A set of polynucleotides in the composition that map to amappable base position in a genome can be non-uniquely tagged, that is,the number of different identifiers can be at least at least 2 and fewerthan the number of polynucleotides that map to the mappable baseposition. A composition of between about 10 ng to about 10 μg (e.g., anyof about 10 ng-1 μg, about 10 ng-100 ng, about 100 ng-10 μg, about 100ng-1 μg, about 1 μg-10 μg) can bear between any of 2, 5, 10, 50 or 100to any of 100, 1000, 10,000 or 100,000 different identifiers. Forexample, between 5 and 100 different identifiers can be used to tag thepolynucleotides in such a composition.

III. Nucleic Acid Sequencing Platforms

After extraction and isolation of cell free polynucleotides from bodilyfluids, cell free sequences may be sequenced. Often, a sequencing methodis classic Sanger sequencing. Sequencing methods may include, but arenot limited to: high-throughput sequencing, pyrosequencing,sequencing-by-synthesis, single-molecule sequencing, nanoporesequencing, semiconductor sequencing, sequencing-by-ligation,sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression(Helicos), Next generation sequencing, Single Molecule Sequencing bySynthesis (SMSS)(Helicos), massively-parallel sequencing, Clonal SingleMolecule Array (Solexa), shotgun sequencing, Maxim-Gilbert sequencing,primer walking, sequencing using PacBio, SOLiD, Ion Torrent, or Nanoporeplatforms and any other sequencing methods known in the art.

In some cases, sequencing reactions various types, as described herein,may comprise a variety of sample processing units. Sample processingunits may include but are not limited to multiple lanes, multiplechannels, multiple wells, or other mean of processing multiple samplesets substantially simultaneously. Additionally, the sample processingunit may include multiple sample chambers to enable processing ofmultiple runs simultaneously.

In some examples, simultaneous sequencing reactions may be performedusing multiplex sequencing. In some cases, cell free polynucleotides maybe sequenced with at least 1000, 2000, 3000, 4000, 5000, 6000, 7000,8000, 9000, 10000, 50000, 100,000 sequencing reactions. In other casescell free poly nucleotides may be sequenced with less than 1000, 2000,3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 50000, 100,000sequencing reactions. Sequencing reactions may be performed sequentiallyor simultaneously. Subsequent data analysis may be performed on all orpart of the sequencing reactions. In some cases, data analysis may beperformed on at least 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000,9000, 10000, 50000, 100,000 sequencing reactions. In other cases dataanalysis may be performed on less than 1000, 2000, 3000, 4000, 5000,6000, 7000, 8000, 9000, 10000, 50000, 100,000 sequencing reactions.

In other examples, the number of sequence reactions may provide coveragefor different amounts of the genome. In some cases, sequence coverage ofthe genome may be at least 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%,70%, 80%, 90%, 95%, 99%, 99.9% or 100%. In other cases, sequencecoverage of the genome may be less than 5%, 10%, 15%, 20%, 25%, 30%,40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% or 100%.

In some examples, sequencing can be performed on cell freepolynucleotides that may comprise a variety of different types ofnucleic acids. Nucleic acids may be polynucleotides or oligonucleotides.Nucleic acids included, but are not limited to DNA or RNA, singlestranded or double stranded or a RNA/cDNA pair.

IV. Polynucleotide Analysis Strategy

FIG. 8. is a diagram, 800, showing a strategy for analyzingpolynucleotides in a sample of initial genetic material. In step 802, asample containing initial genetic material is provided. The sample caninclude target nucleic acid in low abundance. For example, nucleic acidfrom a normal or wild-type genome (e.g., a germline genome) canpredominate in a sample that also includes no more than 20%, no morethan 10%, no more than 5%, no more than 1%, no more than 0.5% or no morethan 0.1% nucleic acid from at least one other genome containing geneticvariation, e.g., a cancer genome or a fetal genome, or a genome fromanother species. The sample can include, for example, cell free nucleicacid or cells comprising nucleic acid. The initial genetic material canconstitute no more than 100 ng nucleic acid. This can contribute toproper oversampling of the original polynucleotides by the sequencing orgenetic analysis process. Alternatively, the sample can be artificiallycapped or bottlenecked to reduce the amount of nucleic acid to no morethan 100 ng or selectively enriched to analyze only sequences ofinterest. The sample can be modified to selectively produce sequencereads of molecules mapping to each of one or more selected locations ina reference sequence. A sample of 100 ng of nucleic acid can containabout 30,000 human haploid genome equivalents, that is, molecules that,together, provide 30,000-fold coverage of a human genome.

In step 804 the initial genetic material is converted into a set oftagged parent polynucleotides. Tagging can include attaching sequencedtags to molecules in the initial genetic material. Sequenced tags can beselected so that all unique polynucleotides mapping to the same locationin a reference sequence had a unique identifying tag. Conversion can beperformed at high efficiency, for example at least 50%.

In step 806, the set of tagged parent polynucleotides is amplified toproduce a set of amplified progeny polynucleotides. Amplification maybe, for example, 1,000-fold.

In step 808, the set of amplified progeny polynucleotides are sampledfor sequencing. The sampling rate is chosen so that the sequence readsproduced both (1) cover a target number of unique molecules in the setof tagged parent polynucleotides and (2) cover unique molecules in theset of tagged parent polynucleotides at a target coverage fold (e.g.,5-to 10-fold coverage of parent polynucleotides.

In step 810, the set of sequence reads is collapsed to produce a set ofconsensus sequences corresponding to unique tagged parentpolynucleotides. Sequence reads can be qualified for inclusion in theanalysis. For example, sequence reads that fail to meet a qualitycontrol scores can be removed from the pool. Sequence reads can besorted into families representing reads of progeny molecules derivedfrom a particular unique parent molecule. For example, a family ofamplified progeny polynucleotides can constitute those amplifiedmolecules derived from a single parent polynucleotide. By comparingsequences of progeny in a family, a consensus sequence of the originalparent polynucleotide can be deduced. This produces a set of consensussequences representing unique parent polynucleotides in the tagged pool.

In step 812, the set of consensus sequences is analyzed using any of theanalytical methods described herein. For example, consensus sequencesmapping to a particular reference sequence location can be analyzed todetect instances of genetic variation. Consensus sequences mapping toparticular reference sequences can be measured and normalized againstcontrol samples. Measures of molecules mapping to reference sequencescan be compared across a genome to identify areas in the genome in whichcopy number varies, or heterozygosity is lost.

FIG. 9 is a diagram presenting a more generic method of extractinginformation from a signal represented by a collection of sequence reads.In this method, after sequencing amplified progeny polynucleotides, thesequence reads are grouped into families of molecules amplified from amolecule of unique identity (910). This grouping can be a jumping offpoint for methods of interpreting the information in the sequence todetermine the contents of the tagged parent polynucleotides with higherfidelity, e.g., less noise and/or distortion.

Analysis of the collection of sequence reads allows one to makeinferences about the parent polynucleotide population from which thesequence reads were generated. Such inferences may be useful becausesequencing typically involves reading only a partial subset of theglobal total amplified polynucleotides. Therefore, one cannot be certainthat every parent polynucleotide will be represented by at least onesequence read in the collection of sequence reads.

One such inference is the number of unique parent polynucleotides in theoriginal pool. Such an inference can be made based on the number ofunique families into which the sequence reads can be grouped and thenumber of sequence reads in each family. In this case, a family refersto a collection of sequence reads traceable back to an original parentpolynucleotide. The inference can be made using well-known statisticalmethods. For example, if grouping produces many families, eachrepresented by one or a few progeny, then one can infer that theoriginal population included more unique parent polynucleotides thatwere not sequenced. On the other hand, if grouping produces only a fewfamilies, each family represented by many progeny, then one can inferthat most of the unique polynucleotides in the parent population arerepresented by at least one sequence read group into that family.

Another such inference is the frequency of a base or sequence of basesat a particular locus in an original pool of polynucleotides. Such aninference can be made based on the number of unique families into whichthe sequence reads can be grouped and the number of sequence reads ineach family. Analyzing the base calls at a locus in a family of sequencereads, a confidence score is assigned to each particular base call orsequence. Then, taking into consideration the confidence score for eachbase call in a plurality of the families, the frequency of each base orsequence at the locus is determined.

V. Copy Number Variation Detection A. Copy Number Variation DetectionUsing Single Sample

FIG. 1. is a diagram, 100, showing a strategy for detection of copynumber variation in a single subject. As shown herein, copy numbervariation detection methods can be implemented as follows. Afterextraction and isolation of cell free polynucleotides in step 102, asingle unique sample can be sequenced by a nucleic acid sequencingplatform known in the art in step 104. This step generates a pluralityof genomic fragment sequence reads. In some cases, these sequences readsmay contain barcode information. In other examples, barcodes are notutilized. After sequencing, reads are assigned a quality score. Aquality score may be a representation of reads that indicates whetherthose reads may be useful in subsequent analysis based on a threshold.In some cases, some reads are not of sufficient quality or length toperform the subsequent mapping step. Sequencing reads with a qualityscore at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filteredout of the data. In other cases, sequencing reads assigned a qualityscored less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filteredout of the data set. In step 106, the genomic fragment reads that meet aspecified quality score threshold are mapped to a reference genome, or atemplate sequence that is known not to contain copy number variations.After mapping alignment, sequence reads are assigned a mapping score. Amapping score may be a representation or reads mapped back to thereference sequence indicating whether each position is or is notuniquely mappable. In instances, reads may be sequences unrelated tocopy number variation analysis. For example, some sequence reads mayoriginate from contaminant polynucleotides. Sequencing reads with amapping score at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may befiltered out of the data set. In other cases, sequencing reads assigneda mapping scored less than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% maybe filtered out of the data set.

After data filtering and mapping, the plurality of sequence readsgenerates a chromosomal region of coverage. In step 108 thesechromosomal regions may be divided into variable length windows or bins.A window or bin may be at least 5 kb, 10, kb, 25 kb, 30 kb, 35, kb, 40kb, 50 kb, 60 kb, 75 kb, 100 kb, 150 kb, 200 kb, 500 kb, or 1000 kb. Awindow or bin may also have bases up to 5 kb, 10, kb, 25 kb, 30 kb, 35,kb, 40 kb, 50 kb, 60 kb, 75 kb, 100 kb, 150 kb, 200 kb, 500 kb, or 1000kb. A window or bin may also be about 5 kb, 10, kb, 25 kb, 30 kb, 35,kb, 40 kb, 50 kb, 60 kb, 75 kb, 100 kb, 150 kb, 200 kb, 500 kb, or 1000kb.

For coverage normalization in step 110, each window or bin is selectedto contain about the same number of mappable bases. In some cases, eachwindow or bin in a chromosomal region may contain the exact number ofmappable bases. In other cases, each window or bin may contain adifferent number of mappable bases. Additionally, each window or bin maybe non-overlapping with an adjacent window or bin. In other cases, awindow or bin may overlap with another adjacent window or bin. In somecases a window or bin may overlap by at least 1 bp, 2, bp, 3 bp, 4 bp,5, bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp, or1000 bp. In other cases, a window or bin may overlap by up to 1 bp, 2,bp, 3 bp, 4 bp, 5, bp, 10 bp, 20 bp, 25 bp, 50 bp, 100 bp, 200 bp, 250bp, 500 bp, or 1000 bp. In some cases a window or bin may overlap byabout 1 bp, 2, bp, 3 bp, 4 bp, 5, bp, 10 bp, 20 bp, 25 bp, 50 bp, 100bp, 200 bp, 250 bp, 500 bp, or 1000 bp.

In some cases, each of the window regions may be sized so they containabout the same number of uniquely mappable bases. The mappability ofeach base that comprise a window region is determined and used togenerate a mappability file which contains a representation of readsfrom the references that are mapped back to the reference for each file.The mappability file contains one row per every position, indicatingwhether each position is or is not uniquely mappable.

Additionally, predefined windows, known throughout the genome to be hardto sequence, or contain a substantially high GC bias, may be filteredfrom the data set. For example, regions known to fall near thecentromere of chromosomes (i.e., centromeric DNA) are known to containhighly repetitive sequences that may produce false positive results.These regions may be filtered out. Other regions of the genome, such asregions that contain an unusually high concentration of other highlyrepetitive sequences such as microsatellite DNA, may be filtered fromthe data set.

The number of windows analyzed may also vary. In some cases, at least10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000,50,000 or 100,000 windows are analyzed. In other cases, the number ofwidows analyzed is up to 10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000,5,000, 10,000, 20,000, 50,000 or 100,000 windows are analyzed.

For an exemplary genome derived from cell free polynucleotide sequences,the next step comprises determining read coverage for each windowregion. This may be performed using either reads with barcodes, orwithout barcodes. In cases without barcodes, the previous mapping stepswill provide coverage of different base positions. Sequence reads thathave sufficient mapping and quality scores and fall within chromosomewindows that are not filtered, may be counted. The number of coveragereads may be assigned a score per each mappable position. In casesinvolving barcodes, all sequences with the same barcode, physicalproperties or combination of the two may be collapsed into one read, asthey are all derived from the sample parent molecule. This step reducesbiases which may have been introduced during any of the preceding steps,such as steps involving amplification. For example, if one molecule isamplified 10 times but another is amplified 1000 times, each molecule isonly represented once after collapse thereby negating the effect ofuneven amplification. Only reads with unique barcodes may be counted foreach mappable position and influence the assigned score.

Consensus sequences can be generated from families of sequence reads byany method known in the art. Such methods include, for example, linearor non-linear methods of building consensus sequences (such as voting,averaging, statistical, maximum a posteriori or maximum likelihooddetection, dynamic programming, Bayesian, hidden Markov or supportvector machine methods, etc.) derived from digital communication theory,information theory, or bioinformatics.

After the sequence read coverage has been determined, a stochasticmodeling algorithm is applied to convert the normalized nucleic acidsequence read coverage for each window region to the discrete copynumber states. In some cases, this algorithm may comprise one or more ofthe following: Hidden Markov Model, dynamic programming, support vectormachine, Bayesian network, trellis decoding, Viterbi decoding,expectation maximization, Kalman filtering methodologies and neuralnetworks.

In step 112, the discrete copy number states of each window region canbe utilized to identify copy number variation in the chromosomalregions. In some cases, all adjacent window regions with the same copynumber can be merged into a segment to report the presence or absence ofcopy number variation state. In some cases, various windows can befiltered before they are merged with other segments.

In step 114, the copy number variation may be reported as graph,indicating various positions in the genome and a corresponding increaseor decrease or maintenance of copy number variation at each respectiveposition. Additionally, copy number variation may be used to report apercentage score indicating how much disease material (or nucleic acidshaving a copy number variation) exists in the cell free polynucleotidesample.

One method of determining copy number variation is shown in FIG. 10. Inthat method, after grouping sequence reads into families generated froma single parent polynucleotide (1010), the families are quantified, forexample, by determining the number of families mapping to each of aplurality of different reference sequence locations. CNVs can bedetermined directly by comparing a quantitative measure of families ateach of a plurality of different loci (1016b). Alternatively, one caninfer a quantitative measure of families in the population of taggedparent polynucleotides using both a quantitative measure of families anda quantitative measure of family members in each family, e.g., asdiscussed above. Then, CNV can be determined by comparing the inferredmeasure of quantity at the plurality of loci. In other embodiments, ahybrid approach can be taken whereby a similar inference of originalquantity can be made following normalization for representational biasduring the sequencing process, such as GC bias, etc

B. Copy Number Variation Detection Using Paired Sample

Paired sample copy number variation detection shares many of the stepsand parameters as the single sample approach described herein. However,as depicted in 200 of FIG. 2 of copy number variation detection usingpaired samples requires comparison of sequence coverage to a controlsample rather than comparing it the predicted mappability of the genome.This approach may aid in normalization across windows.

FIG. 2. is a diagram, 200 showing a strategy for detection of copynumber variation in paired subject. As shown herein, copy numbervariation detection methods can be implemented as follows. In step 204,a single unique sample can be sequenced by a nucleic acid sequencingplatform known in the art after extraction and isolation of the samplein step 202. This step generates a plurality of genomic fragmentsequence reads. Additionally, a sample or control sample is taken fromanother subject. In some cases, the control subject may be a subject notknown to have disease, whereas the other subject may have or be at riskfor a particular disease. In some cases, these sequences reads maycontain barcode information. In other examples, barcodes are notutilized. After sequencing, reads are assigned a quality score. In somecases, some reads are not of sufficient quality or length to perform thesubsequent mapping step. Sequencing reads with a quality score at least90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of the dataset. In other cases, sequencing reads assigned a quality scored lessthan 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of thedata set. In step 206, the genomic fragment reads that meet a specifiedquality score threshold are mapped to a reference genome, or a templatesequence that is known not to contain copy number variations. Aftermapping alignment, sequence reads are assigned a mapping score. Ininstances, reads may be sequences unrelated to copy number variationanalysis. For example, some sequence reads may originate fromcontaminant polynucleotides. Sequencing reads with a mapping score atleast 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out of thedata set. In other cases, sequencing reads assigned a mapping scoredless than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out ofthe data set.

After data filtering and mapping, the plurality of sequence readsgenerates a chromosomal region of coverage for each of the test andcontrol subjects. In step 208 these chromosomal regions may be dividedinto variable length windows or bins. A window or bin may be at least 5kb, 10, kb, 25 kb, 30 kb, 35, kb, 40 kb, 50 kb, 60 kb, 75 kb, 100 kb,150 kb, 200 kb, 500 kb, or 1000 kb. A window or bin may also be lessthan 5 kb, 10, kb, 25 kb, 30 kb, 35, kb, 40 kb, 50 kb, 60 kb, 75 kb, 100kb, 150 kb, 200 kb, 500 kb, or 1000 kb.

For coverage normalization in step 210, each window or bin is selectedto contain about the same number of mappable bases for each of the testand control subjects. In some cases, each window or bin in a chromosomalregion may contain the exact number of mappable bases. In other cases,each window or bin may contain a different number of mappable bases.Additionally, each window or bin may be non-overlapping with an adjacentwindow or bin. In other cases, a window or bin may overlap with anotheradjacent window or bin. In some cases a window or bin may overlap by atleast 1 bp, 2, bp, 3 bp, 4 bp, 5, bp, 10 bp, 20 bp, 25 bp, 50 bp, 100bp, 200 bp, 250 bp, 500 bp, or 1000 bp. In other cases, a window or binmay overlap by less than 1 bp, 2, bp, 3 bp, 4 bp, 5, bp, 10 bp, 20 bp,25 bp, 50 bp, 100 bp, 200 bp, 250 bp, 500 bp, or 1000 bp.

In some cases, each of the window regions is sized so they contain aboutthe same number of uniquely mappable bases for each of the test andcontrol subjects. The mappability of each base that comprise a windowregion is determined and used to generate a mappability file whichcontains a representation of reads from the references that are mappedback to the reference for each file. The mappability file contains onerow per every position, indicating whether each position is or is notuniquely mappable.

Additionally, predefined windows, known throughout the genome to be hardto sequence, or contain a substantially high GC bias, are filtered fromthe data set. For example, regions known to fall near the centromere ofchromosomes (i.e., centromeric DNA) are known to contain highlyrepetitive sequences that may produce false positive results. Theseregions may be filtered. Other regions of the genome, such as regionsthat contain an unusually high concentration of other highly repetitivesequences such as microsatellite DNA, may be filtered from the data set.

The number of windows analyzed may also vary. In some cases, at least10, 20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000,50,000 or 100,000 windows are analyzed. In other cases, less than 10,20, 30, 40, 50, 100, 200, 500, 1000, 2000, 5,000, 10,000, 20,000, 50,000or 100,000 windows are analyzed.

For an exemplary genome derived from cell free polynucleotide sequences,the next step comprises determining read coverage for each window regionfor each of the test and control subjects. This may be performed usingeither reads with barcodes, or without barcodes. In cases withoutbarcodes, the pervious mapping steps will provide coverage of differentbase positions. Sequence reads that have sufficient mapping and qualityscores and fall within chromosome windows that are not filtered, may becounted. The number of coverage reads may be assigned a score per eachmappable position. In cases involving barcodes, all sequences with thesame barcode may be collapsed into one read, as they are all derivedfrom the sample parent molecule. This step reduces biases which may havebeen introduced during any of the preceding steps, such as stepsinvolving amplification. Only reads with unique barcodes may be countedfor each mappable position and influence the assigned score. For thisreason, it is important that the barcode ligation step be performed in amanner optimized for producing the lowest amount of bias.

In determining the nucleic acid read coverage for each window, thecoverage of each window can be normalized by the mean coverage of thatsample. Using such an approach, it may be desirable to sequence both thetest subject and the control under similar conditions. The read coveragefor each window may be then expressed as a ratio across similar windows

Nucleic acid read coverage ratios for each window of the test subjectcan be determined by dividing the read coverage of each window region ofthe test sample with read coverage of a corresponding window region ofthe control ample.

After the sequence read coverage ratios have been determined, astochastic modeling algorithm is applied to convert the normalizedratios for each window region into discrete copy number states. In somecases, this algorithm may comprise a Hidden Markov Model. In othercases, the stochastic model may comprise dynamic programming, supportvector machine, Bayesian modeling, probabilistic modeling, trellisdecoding, Viterbi decoding, expectation maximization, Kalman filteringmethodologies, or neural networks.

In step 212, the discrete copy number states of each window region canbe utilized to identify copy number variation in the chromosomalregions. In some cases, all adjacent window regions with the same copynumber can be merged into a segment to report the presence or absence ofcopy number variation state. In some cases, various windows can befiltered before they are merged with other segments.

In step 214, the copy number variation may be reported as graph,indicating various positions in the genome and a corresponding increaseor decrease or maintenance of copy number variation at each respectiveposition. Additionally, copy number variation may be used to report apercentage score indicating how much disease material exists in the cellfree poly nucleotide sample.

VI. Rare mutation Detection

Rare mutation detection shares similar features as both copy numbervariation approaches. However, as depicted in FIG. 3, 300, rare mutationdetection uses comparison of sequence coverage to a control sample orreference sequence rather than comparing it the relative mappability ofthe genome. This approach may aid in normalization across windows.

Generally, rare mutation detection may be performed on selectivelyenriched regions of the genome or transcriptome purified and isolated instep 302. As described herein, specific regions, which may include butare not limited to genes, oncogenes, tumor suppressor genes, promoters,regulatory sequence elements, non-coding regions, miRNAs, snRNAs and thelike may be selectively amplified from a total population of cell freepolynucleotides. This may be performed as herein described. In oneexample, multiplex sequencing may be used, with or without barcodelabels for individual polynucleotide sequences. In other examples,sequencing may be performed using any nucleic acid sequencing platformsknown in the art. This step generates a plurality of genomic fragmentsequence reads as in step 304. Additionally, a reference sequence isobtained from a control sample, taken from another subject. In somecases, the control subject may be a subject known to not have knowngenetic aberrations or disease. In some cases, these sequence reads maycontain barcode information. In other examples, barcodes are notutilized. After sequencing, reads are assigned a quality score. Aquality score may be a representation of reads that indicates whetherthose reads may be useful in subsequent analysis based on a threshold.In some cases, some reads are not of sufficient quality or length toperform the subsequent mapping step. Sequencing reads with a qualityscore at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filteredout of the data set. In other cases, sequencing reads assigned a qualityscored at least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filteredout of the data set. In step 306, the genomic fragment reads that meet aspecified quality score threshold are mapped to a reference genome, or areference sequence that is known not to contain rare mutations. Aftermapping alignment, sequence reads are assigned a mapping score. Amapping score may be a representation or reads mapped back to thereference sequence indicating whether each position is or is notuniquely mappable. In instances, reads may be sequences unrelated torare mutation analysis. For example, some sequence reads may originatefrom contaminant polynucleotides. Sequencing reads with a mapping scoreat least 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out ofthe data set. In other cases, sequencing reads assigned a mapping scoredless than 90%, 95%, 99%, 99.9%, 99.99% or 99.999% may be filtered out ofthe data set.

For each mappable base, bases that do not meet the minimum threshold formappability, or low quality bases, may be replaced by the correspondingbases as found in the reference sequence.

After data filtering and mapping, variant bases found between thesequence reads obtained from the subject and the reference sequence areanalyzed.

For an exemplary genome derived from cell free polynucleotide sequences,the next step comprises determining read coverage for each mappable baseposition. This may be performed using either reads with barcodes, orwithout barcodes. In cases without barcodes, the previous mapping stepswill provide coverage of different base positions. Sequence reads thathave sufficient mapping and quality scores may be counted. The number ofcoverage reads may be assigned a score per each mappable position. Incases involving barcodes, all sequences with the same barcode may becollapsed into one consensus read, as they are all derived from thesample parent molecule. The sequence for each base is aligned as themost dominant nucleotide read for that specific location. Further, thenumber of unique molecules can be counted at each position to derivesimultaneous quantification at each position. This step reduces biaseswhich may have been introduced during any of the preceding steps, suchas steps involving amplification. Only reads with unique barcodes may becounted for each mappable position and influence the assigned score.

Once read coverage may be ascertained and variant bases relative to thecontrol sequence in each read are identified, the frequency of variantbases may be calculated as the number of reads containing the variantdivided by the total number of reads. This may be expressed as a ratiofor each mappable position in the genome.

For each base position, the frequencies of all four nucleotides,cytosine, guanine, thymine, adenine are analyzed in comparison to thereference sequence. A stochastic or statistical modeling algorithm isapplied to convert the normalized ratios for each mappable position toreflect frequency states for each base variant. In some cases, thisalgorithm may comprise one or more of the following: Hidden MarkovModel, dynamic programming, support vector machine, Bayesian orprobabilistic modeling, trellis decoding, Viterbi decoding, expectationmaximization, Kalman filtering methodologies, and neural networks.

In step 312, the discrete rare mutation states of each base position canbe utilized to identify a base variant with high frequency of varianceas compared to the baseline of the reference sequence. In some cases,the baseline might represent a frequency of at least 0.0001%, 0.001%,0.01%, 0.1%, 1.0%, 2.0%, 3.0%, 4.0% 5.0%, 10%, or 25%. In other casesthe baseline might represent a frequency of at least 0.0001%, 0.001%,0.01%, 0.1%, 1.0%, 2.0%, 3.0%, 4.0% 5.0%. 10%, or 25%. In some cases,all adjacent base positions with the base variant or mutation can bemerged into a segment to report the presence or absence of a raremutation. In some cases, various positions can be filtered before theyare merged with other segments.

After calculation of frequencies of variance for each base position, thevariant with largest deviation for a specific position in the sequencederived from the subject as compared to the reference sequence isidentified as a rare mutation. In some cases, a rare mutation may be acancer mutation. In other cases, a rare mutation might be correlatedwith a disease state.

A rare mutation or variant may comprise a genetic aberration thatincludes, but is not limited to a single base substitution, or smallindels, transversions, translocations, inversion, deletions, truncationsor gene truncations. In some cases, a rare mutation may be at most 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 nucleotides in length. On other casesa rare mutation may be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20nucleotides in length.

In step 314, the presence or absence of a mutation may be reflected ingraphical form, indicating various positions in the genome and acorresponding increase or decrease or maintenance of a frequency ofmutation at each respective position. Additionally, rare mutations maybe used to report a percentage score indicating how much diseasematerial exists in the cell free polynucleotide sample. A confidencescore may accompany each detected mutation, given known statistics oftypical variances at reported positions in non-disease referencesequences. Mutations may also be ranked in order of abundance in thesubject or ranked by clinically actionable importance.

FIG. 11 shows a method of inferring the frequency of a base or sequenceof bases at a particular locus in a population polynucleotides. Sequencereads are grouped into families generated from an original taggedpolynucleotide (1110). For each family, one or more bases at the locusis each assigned a confidence score. The confidence score can beassigned by any of a number of known statistical methods is assigned andcan be based, at least in part, on the frequency at which a base appearsamong the sequence reads belonging to the family (1112). For example,the confidence score can be the frequency at which the base appearsamong the sequence reads. As another example, for each family, a hiddenMarkov model can be built, such that a maximum likelihood or maximum aposteriori decision can be made based on the frequency of occurrence ofa particular base in a single family. As part of this model, theprobability of error and resultant confidence score for a particulardecision can be output as well. A frequency of the base in the originalpopulation can then be assigned based on the confidence scores among thefamilies (1114).

VII. Applications

A. Early Detection of Cancer

Numerous cancers may be detected using the methods and systems describedherein. Cancers cells, as most cells, can be characterized by a rate ofturnover, in which old cells die and replaced by newer cells. Generallydead cells, in contact with vasculature in a given subject, may releaseDNA or fragments of DNA into the blood stream. This is also true ofcancer cells during various stages of the disease. Cancer cells may alsobe characterized, dependent on the stage of the disease, by variousgenetic aberrations such as copy number variation as well as raremutations. This phenomenon may be used to detect the presence or absenceof cancers individuals using the methods and systems described herein.

For example, blood from subjects at risk for cancer may be drawn andprepared as described herein to generate a population of cell freepolynucleotides. In one example, this might be cell free DNA. Thesystems and methods of the disclosure may be employed to detect raremutations or copy number variations that may exist in certain cancerspresent. The method may help detect the presence of cancerous cells inthe body, despite the absence of symptoms or other hallmarks of disease.

The types and number of cancers that may be detected may include but arenot limited to blood cancers, brain cancers, lung cancers, skin cancers,nose cancers, throat cancers, liver cancers, bone cancers, lymphomas,pancreatic cancers, skin cancers, bowel cancers, rectal cancers, thyroidcancers, bladder cancers, kidney cancers, mouth cancers, stomachcancers, solid state tumors, heterogeneous tumors, homogenous tumors andthe like.

In the early detection of cancers, any of the systems or methods hereindescribed, including rare mutation detection or copy number variationdetection may be utilized to detect cancers. These system and methodsmay be used to detect any number of genetic aberrations that may causeor result from cancers. These may include but are not limited tomutations, rare mutations, indels, copy number variations,transversions, translocations, inversion, deletions, aneuploidy, partialaneuploidy, polyploidy, chromosomal instability, chromosomal structurealterations, gene fusions, chromosome fusions, gene truncations, geneamplification, gene duplications, chromosomal lesions, DNA lesions,abnormal changes in nucleic acid chemical modifications, abnormalchanges in epigenetic patterns, abnormal changes in nucleic acidmethylation infection and cancer.

Additionally, the systems and methods described herein may also be usedto help characterize certain cancers. Genetic data produced from thesystem and methods of this disclosure may allow practitioners to helpbetter characterize a specific form of cancer. Often times, cancers areheterogeneous in both composition and staging. Genetic profile data mayallow characterization of specific sub-types of cancer that may beimportant in the diagnosis or treatment of that specific sub-type. Thisinformation may also provide a subject or practitioner clues regardingthe prognosis of a specific type of cancer.

B. Cancer Monitoring and Prognosis

The systems and methods provided herein may be used to monitor alreadyknown cancers, or other diseases in a particular subject. This may alloweither a subject or practitioner to adapt treatment options in accordwith the progress of the disease. In this example, the systems andmethods described herein may be used to construct genetic profiles of aparticular subject of the course of the disease. In some instances,cancers can progress, becoming more aggressive and genetically unstable.In other examples, cancers may remain benign, inactive, dormant or inremission. The system and methods of this disclosure may be useful indetermining disease progression, remission or recurrence.

Further, the systems and methods described herein may be useful indetermining the efficacy of a particular treatment option. In oneexample, successful treatment options may actually increase the amountof copy number variation or rare mutations detected in subject's bloodif the treatment is successful as more cancers may die and shed DNA. Inother examples, this may not occur. In another example, perhaps certaintreatment options may be correlated with genetic profiles of cancersover time. This correlation may be useful in selecting a therapy.Additionally, if a cancer is observed to be in remission aftertreatment, the systems and methods described herein may be useful inmonitoring residual disease or recurrence of disease.

For example, mutations occurring within a range of frequency beginningat threshold level can be determined from DNA in a sample from asubject, e.g., a patient. The mutations can be, e.g., cancer relatedmutations. The frequency can range from, for example, at least 0.1%, atleast 1%, or at least 5% to 100%. The sample can be, e.g., cell free DNAor a tumor sample. A course of treatment can be prescribed based on anyor all of mutations occurring within the frequency range including,e.g., their frequencies. A sample can be taken from the subject at anysubsequent time. Mutations occurring within the original range offrequency or a different range of frequency can be determined. Thecourse of treatment can be adjusted based on the subsequentmeasurements.

C. Early Detection and Monitoring of Other Diseases or Disease States

The methods and systems described herein may not be limited to detectionof rare mutations and copy number variations associated with onlycancers. Various other diseases and infections may result in other typesof conditions that may be suitable for early detection and monitoring.For example, in certain cases, genetic disorders or infectious diseasesmay cause a certain genetic mosaicism within a subject. This geneticmosaicism may cause copy number variation and rare mutations that couldbe observed. In another example, the system and methods of thedisclosure may also be used to monitor the genomes of immune cellswithin the body. Immune cells, such as B cells, may undergo rapid clonalexpansion upon the presence certain diseases. Clonal expansions may bemonitored using copy number variation detection and certain immunestates may be monitored. In this example, copy number variation analysismay be performed over time to produce a profile of how a particulardisease may be progressing.

Further, the systems and methods of this disclosure may also be used tomonitor systemic infections themselves, as may be caused by a pathogensuch as a bacteria or virus. Copy number variation or even rare mutationdetection may be used to determine how a population of pathogens arechanging during the course of infection. This may be particularlyimportant during chronic infections, such as HIV/AIDs or Hepatitisinfections, whereby viruses may change life cycle state and/or mutateinto more virulent forms during the course of infection.

Yet another example that the system and methods of this disclosure maybe used for is the monitoring of transplant subjects. Generally,transplanted tissue undergoes a certain degree of rejection by the bodyupon transplantation. The methods of this disclosure may be used todetermine or profile rejection activities of the host body, as immunecells attempt to destroy transplanted tissue. This may be useful inmonitoring the status of transplanted tissue as well as altering thecourse of treatment or prevention of rejection.

Further, the methods of the disclosure may be used to characterize theheterogeneity of an abnormal condition in a subject, the methodcomprising generating a genetic profile of extracellular polynucleotidesin the subject, wherein the genetic profile comprises a plurality ofdata resulting from copy number variation and rare mutation analyses. Insome cases, including but not limited to cancer, a disease may beheterogeneous. Disease cells may not be identical. In the example ofcancer, some tumors are known to comprise different types of tumorcells, some cells in different stages of the cancer. In other examples,heterogeneity may comprise multiple foci of disease. Again, in theexample of cancer, there may be multiple tumor foci, perhaps where oneor more foci are the result of metastases that have spread from aprimary site.

The methods of this disclosure may be used to generate or profile,fingerprint or set of data that is a summation of genetic informationderived from different cells in a heterogeneous disease. This set ofdata may comprise copy number variation and rare mutation analyses aloneor in combination.

D. Early Detection and Monitoring of Other Diseases or Disease States ofFetal Origin

Additionally, the systems and methods of the disclosure may be used todiagnose, prognose, monitor or observe cancers or other diseases offetal origin. That is, these methodologies may be employed in a pregnantsubject to diagnose, prognose, monitor or observe cancers or otherdiseases in a unborn subject whose DNA and other polynucleotides mayco-circulate with maternal molecules.

VIII. Terminology

The terminology used therein is for the purpose of describing particularembodiments only and is not intended to be limiting of a systems andmethods of this disclosure. As used herein, the singular forms “a”, “an”and “the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. Furthermore, to the extent that theterms “including”, “includes”, “having”, “has”, “with”, or variantsthereof are used in either the detailed description and/or the claims,such terms are intended to be inclusive in a manner similar to the term“comprising”.

Several aspects of a systems and methods of this disclosure aredescribed above with reference to example applications for illustration.It should be understood that numerous specific details, relationships,and methods are set forth to provide a full understanding of a systemsand methods. One having ordinary skill in the relevant art, however,will readily recognize that a systems and methods can be practicedwithout one or more of the specific details or with other methods. Thisdisclosure is not limited by the illustrated ordering of acts or events,as some acts may occur in different orders and/or concurrently withother acts or events. Furthermore, not all illustrated acts or eventsare required to implement a methodology in accordance with thisdisclosure.

Ranges can be expressed herein as from “about” one particular value,and/or to “about” another particular value. When such a range isexpressed, another embodiment includes from the one particular valueand/or to the other particular value. Similarly, when values areexpressed as approximations, by use of the antecedent “about,” it willbe understood that the particular value forms another embodiment. Itwill be further understood that the endpoints of each of the ranges aresignificant both in relation to the other endpoint, and independently ofthe other endpoint. The term “about” as used herein refers to a rangethat is 15% plus or minus from a stated numerical value within thecontext of the particular usage. For example, about 10 would include arange from 8.5 to 11.5.

Computer systems

Methods of the present disclosure can be implemented using, or with theaid of, computer systems. FIG. 15 shows a computer system 1501 that isprogrammed or otherwise configured to implement the methods of thepresent disclosure. The computer system 1501 can regulate variousaspects sample preparation, sequencing and/or analysis. In someexamples, the computer system 1501 is configured to perform samplepreparation and sample analysis, including nucleic acid sequencing.

The computer system 1501 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1505, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 1501 also includes memory or memorylocation 1510 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 1515 (e.g., hard disk), communicationinterface 1520 (e.g., network adapter) for communicating with one ormore other systems, and peripheral devices 1525, such as cache, othermemory, data storage and/or electronic display adapters. The memory1510, storage unit 1515, interface 1520 and peripheral devices 1525 arein communication with the CPU 1505 through a communication bus (solidlines), such as a motherboard. The storage unit 1515 can be a datastorage unit (or data repository) for storing data. The computer system1501 can be operatively coupled to a computer network (“network”) 1530with the aid of the communication interface 1520. The network 1530 canbe the Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 1530 insome cases is a telecommunication and/or data network. The network 1530can include one or more computer servers, which can enable distributedcomputing, such as cloud computing. The network 1530, in some cases withthe aid of the computer system 1501, can implement a peer-to-peernetwork, which may enable devices coupled to the computer system 1501 tobehave as a client or a server.

The CPU 1505 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 1510. Examples ofoperations performed by the CPU 1505 can include fetch, decode, execute,and writeback.

The storage unit 1515 can store files, such as drivers, libraries andsaved programs. The storage unit 1515 can store programs generated byusers and recorded sessions, as well as output(s) associated with theprograms. The storage unit 1515 can store user data, e.g., userpreferences and user programs. The computer system 1501 in some casescan include one or more additional data storage units that are externalto the computer system 1501, such as located on a remote server that isin communication with the computer system 1501 through an intranet orthe Internet.

The computer system 1501 can communicate with one or more remotecomputer systems through the network 1530. For instance, the computersystem 1501 can communicate with a remote computer system of a user(e.g., operator). Examples of remote computer systems include personalcomputers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad,Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone,Android-enabled device, Blackberry®), or personal digital assistants.The user can access the computer system 1501 via the network 1530.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 1501, such as, for example, on thememory 1510 or electronic storage unit 1515. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1505. In some cases, thecode can be retrieved from the storage unit 1515 and stored on thememory 1510 for ready access by the processor 1505. In some situations,the electronic storage unit 1515 can be precluded, andmachine-executable instructions are stored on memory 1510.

The code can be pre-compiled and configured for use with a machine havea processer adapted to execute the code, or can be compiled duringruntime. The code can be supplied in a programming language that can beselected to enable the code to execute in a pre-compiled or as-compiledfashion.

Aspects of the systems and methods provided herein, such as the computersystem 1501, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such memory (e.g., read-only memory, random-access memory,flash memory) or a hard disk. “Storage” type media can include any orall of the tangible memory of the computers, processors or the like, orassociated modules thereof, such as various semiconductor memories, tapedrives, disk drives and the like, which may provide non-transitorystorage at any time for the software programming. All or portions of thesoftware may at times be communicated through the Internet or variousother telecommunication networks. Such communications, for example, mayenable loading of the software from one computer or processor intoanother, for example, from a management server or host computer into thecomputer platform of an application server. Thus, another type of mediathat may bear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to non-transitory, tangible “storage” media, terms such ascomputer or machine “readable medium” refer to any medium thatparticipates in providing instructions to a processor for execution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 1501 can include or be in communication with anelectronic display that comprises a user interface (UI) for providing,for example, one or more results of sample analysis. Examples of UI'sinclude, without limitation, a graphical user interface (GUI) andweb-based user interface.

EXAMPLES Example 1 Prostate Cancer Prognosis and Treatment

A blood sample is taken from a prostate cancer subject. Previously, anoncologist determines that the subject has stage II prostate cancer andrecommends a treatment. Cell free DNA is extracted, isolated, sequencedand analyzed every 6 months after the initial diagnosis.

Cell free DNA is extracted and isolated from blood using the QiagenQubit kit protocol. A carrier DNA is added to increase yields. DNA isamplified using PCR and universal primers. 10 ng of DNA is sequencedusing a massively parallel sequencing approach with an Illumina MiSeqpersonal sequencer. 90% of the subject's genome is covered throughsequencing of cell free DNA.

Sequence data is assembled and analyzed for copy number variation.Sequence reads are mapped and compared to a healthy individual(control). Based on the number of sequence reads, chromosomal regionsare divided into 50 kb non overlapping regions. Sequence reads arecompared to one another and a ratio is determined for each mappableposition.

A Hidden Markov Model is applied to convert copy numbers into discretestates for each window.

Reports are generated, mapping genome positions and copy numbervariation show in FIG. 4A (for a healthy individual) and FIG. 4B for thesubject with cancer.

These reports, in comparison to other profiles of subjects with knownoutcomes, indicate that this particular cancer is aggressive andresistant to treatment. The cell free tumor burden is 21%. The subjectis monitored for 18 months. At month 18, the copy number variationprofile begins to increase dramatically, from cell free tumor burden of21% to 30%. A comparison is done with genetic profiles of other prostatesubjects. It is determined that this increase in copy number variationindicates that the prostate cancer is advancing from stage II to stageIII. The original treatment regiment as prescribed is no longer treatingthe cancer. A new treatment is prescribed.

Further, these reports are submitted and accessed electronically via theinternet. Analysis of sequence data occurs at a site other than thelocation of the subject. The report is generated and transmitted to thesubject's location. Via an internet enabled computer, the subjectaccesses the reports reflecting his tumor burden (FIG. 4C).

Example 2 Prostate Cancer Remission and Recurrence.

A blood sample is taken from a prostate cancer survivor. The subject hadpreviously undergone numerous rounds of chemotherapy and radiation. Thesubject at the time of testing did not present symptoms or health issuesrelated to the cancer. Standard scans and assays reveal the subject tobe cancer free.

Cell free DNA is extracted and isolated from blood using the QiagenTruSeq kit protocol. A carrier DNA is added to increase yields. DNA isamplified using PCR and universal primers. 10 ng of DNA is sequencedusing a massively parallel sequencing approach with an Illumina MiSeqpersonal sequencer. 12 mer barcodes are added to individual moleculesusing a ligation method.

Sequence data is assembled and analyzed for copy number variation.Sequence reads are mapped and compared to a healthy individual(control). Based on the number of sequence reads, chromosomal regionsare divided into 40 kb non overlapping regions. Sequence reads arecompared to one another and a ratio is determined for each mappableposition.

Non unique barcoded sequences are collapsed into a single read to helpnormalize bias from amplification.

A Hidden Markov Model is applied to convert copy numbers into discretestates for each window.

Reports are generated, mapping genome positions and copy numbervariation shown in FIG. 5A, for a subject with cancer in remission andFIG. 5B for a subject with cancer in recurrence.

This report in comparison to other profiles of subjects with knownoutcomes indicates that at month 18, rare mutation analysis for copynumber variation is detected at cell free tumor burden of 5%. Anoncologist prescribes treatment again.

Example 3 Thyroid Cancer and Treatment

A subject is known to have Stage IV thyroid cancer and undergoesstandard treatment, including radiation therapy with 1-131. CT scans areinconclusive as to whether the radiation therapy is destroying cancerousmasses. Blood is drawn before and after the latest radiation session.

Cell free DNA is extracted and isolated from blood using the QiagenQubit kit protocol. A sample of non specific bulk DNA is added to thesample preparation reactions increase yields.

It is known that the BRAF gene may be mutated at amino acid position 600in this thyroid cancer. From population of cell free DNA, BRAF DNA isselectively amplified using primers specific to the gene. 20 merbarcodes are added to the parent molecule as a control for countingreads.

10 ng of DNA is sequenced using massively parallel sequencing approachwith an Illumina MiSeq personal sequencer.

Sequence data is assembled and analyzed for copy number variationdetection. Sequence reads are mapped and compared to a healthyindividual (control). Based on the number of sequence reads, asdetermined by counting the barcode sequences, chromosomal regions aredivided into 50 kb non overlapping regions. Sequence reads are comparedto one another and a ratio is determined for each mappable position.

A Hidden Markov Model is applied to convert copy numbers into discretestates for each window.

A report is generated, mapping genome positions and copy numbervariation.

The reports generated before and after treatment are compared. The tumorcell burden percentage jumps from 30% to 60% after the radiationsession. The jump in tumor burden is determined to be an increase innecrosis of cancer tissue versus normal tissue as a result of treatment.Oncologists recommend the subject continue the prescribed treatment.

Example 4 Sensitivity of Rare mutation Detection

In order to determine the detection ranges of rare mutation present in apopulation of DNA, mixing experiments are performed. Sequences of DNA,some containing wildtype copies of the genes TP53, HRAS and MET and somecontaining copies with rare mutations in the same genes, are mixedtogether in distinct ratios. DNA mixtures are prepared such that ratiosor percentages of mutant DNA to wildtype DNA range from 100% to 0.01%.

10 ng of DNA is sequenced for each mixing experiment using a massivelyparallel sequencing approach with an Illumina MiSeq personal sequencer.

Sequence data is assembled and analyzed for rare mutation detection.Sequence reads are mapped and compared to a reference sequence(control). Based on the number of sequence reads, the frequency ofvariance for each mappable position is determined.

A Hidden Markov Model is applied to convert frequency of variance foreach mappable position into discrete states for base position.

A report is generated, mapping genome base positions and percentagedetection of the rare mutation over baseline as determined by thereference sequence (FIG. 6A).

The results of various mixing experiments ranging from 0.1% to 100% arerepresented in a logarithmic scale graph, with measured percentage ofDNA with a rare mutation graphed as a function of the actual percentageof DNA with a rare mutation (FIG. 6B). The three genes, TP53, HRAS andMET are represented. A strong linear correlation is found betweenmeasured and expected rare mutation populations. Additionally, a lowersensitivity threshold of about 0.1% of DNA with a rare mutation in apopulation of non mutated DNA is found with these experiments (FIG. 6B).

Example 5 Rare mutation Detection in Prostate Cancer Subject

A subject is thought to have early stage prostate cancer. Other clinicaltests provide inconclusive results. Blood is drawn from the subject andcell free DNA is extracted, isolated, prepared and sequenced.

A panel of various oncogenes and tumor suppressor genes are selected forselective amplification using a TaqMan© PCR kit (Invitrogen) using genespecific primers. DNA regions amplified include DNA containing PIK3CAand TP53 genes.

10 ng of DNA is sequenced using a massively parallel sequencing approachwith an Illumina MiSeq personal sequencer.

Sequence data is assembled and analyzed for rare mutation detection.Sequence reads are mapped and compared to a reference sequence(control). Based on the number of sequence reads, the frequency ofvariance for each mappable position was determined.

A Hidden Markov Model is applied to convert frequency of variance foreach mappable position into discrete states for each base position.

A report is generated, mapping genomic base positions and percentagedetection of the rare mutation over baseline as determined by thereference sequence (FIG. 7A). Rare mutations are found at an incidenceof 5% in two genes, PIK3CA and TP53, respectively, indicating that thesubject has an early stage cancer. Treatment is initiated.

Further, these reports are submitted and accessed electronically via theinternet. Analysis of sequence data occurs at a site other than thelocation of the subject. The report is generated and transmitted to thesubject's location. Via an internet enabled computer, the subjectaccesses the reports reflecting his tumor burden (FIG. 7B).

Example 6 Rare mutation Detection in Colorectal Cancer Subjects

A subject is thought to have mid-stage colorectal cancer. Other clinicaltests provide inconclusive results. Blood is drawn from the subject andcell free DNA is extracted.

10 ng of the cell-free genetic material that is extracted from a singletube of plasma is used. The initial genetic material is converted into aset of tagged parent polynucleotides. The tagging included attachingtags required for sequencing as well as non-unique identifiers fortracking progeny molecules to the parent nucleic acids. The conversionis performed through an optimized ligation reaction as described aboveand conversion yield is confirmed by looking at the size profile ofmolecules post-ligation. Conversion yield is measured as the percentageof starting initial molecules that have both ends ligated with tags.Conversion using this approach is performed at high efficiency, forexample, at least 50%.

The tagged library is PCR-amplified and enriched for genes mostassociated with colorectal cancer, (e.g., KRAS, APC, TP53, etc) and theresulting DNA is sequenced using a massively parallel sequencingapproach with an Illumina MiSeq personal sequencer.

Sequence data is assembled and analyzed for rare mutation detection.Sequence reads are collapsed into familial groups belonging to a parentmolecule (as well as error-corrected upon collapse) and mapped using areference sequence (control). Based on the number of sequence reads, thefrequency of rare variations (substitutions, insertions, deletions, etc)and variations in copy number and heterozygosity (when appropriate) foreach mappable position is determined.

A report is generated, mapping genomic base positions and percentagedetection of the rare mutation over baseline as determined by thereference sequence. Rare mutations are found at an incidence of 0.3-0.4%in two genes, KRAS and FBXW7, respectively, indicating that the subjecthas residual cancer. Treatment is initiated.

Further, these reports are submitted and accessed electronically via theinternet. Analysis of sequence data occurs at a site other than thelocation of the subject. The report is generated and transmitted to thesubject's location. Via an internet enabled computer, the subjectaccesses the reports reflecting his tumor burden.

Example 7 Digital Sequencing Technology

The concentrations of tumor-shed nucleic acids are typically so low thatcurrent next-generation sequencing technologies can only detect suchsignals sporadically or in patients with terminally high tumor burden.The main reason being that such technologies are plagued by error ratesand bias that can be orders of magnitude higher than what is required toreliably detect de novo genetic alterations associated with cancer incirculating DNA. Shown here is a new sequencing methodology, DigitalSequencing Technology (DST), which increases the sensitivity andspecificity of detecting and quantifying rare tumor-derived nucleicacids among germline fragments by at least 1-2 orders of magnitude.

DST architecture is inspired by state-of-the-art digital communicationsystems that combat the high noise and distortion caused by moderncommunication channels and are able to transmit digital informationflawlessly at exceedingly high data rates. Similarly, current next-genworkflows are plagued by extremely high noise and distortion (due tosample-prep, PCR-based amplification and sequencing). Digital sequencingis able to eliminate the error and distortion created by these processesand produce near-perfect representation of all rare variants (includingCNVs).

High-Diversity Library preparation

Unlike conventional sequencing library preparation protocols, wherebythe majority of extracted circulating DNA fragments are lost due toinefficient library conversion, our Digital Sequencing Technologyworkflow enables the vast majority of starting molecules to be convertedand sequenced. This is critically important for detection of rarevariants as there may only be a handful of somatically mutated moleculesin an entire 10 mL tube of blood. The efficient molecular biologyconversion process developed enables the highest possible sensitivityfor detection of rare variants.

Comprehensive Actionable Oncogene Panel

The workflow engineered around the DST platform is flexible and highlytunable as targeted regions can be as small as single exons or as broadas whole exomes (or even whole genomes). A standard panel consists ofall exonic bases of 15 actionable cancer-related genes and coverage ofthe “hot” exons of an additional 36 onco-/tumor-suppressor genes (e.g.,exons containing at least one or more reported somatic mutations inCOSMIC).

Example 8 Analytical Studies

To study the performance of our technology, its sensitivity inanalytical samples was evaluated. We spiked varying amounts of LNCaPcancer cell line DNA into a background of normal cfDNA and were able tosuccessfully detect somatic mutations down to 0.1% sensitivity (see FIG.13).

Preclinical Studies

The concordance of circulating DNA with tumor gDNA in human xenograftmodels in mice was investigated. In seven CTC-negative mice, each withone of two different human breast cancer tumors, all somatic mutationsdetected in tumor gDNA were also detected in mouse blood cfDNA using DSTfurther validating the utility of cfDNA for non-invasive tumor geneticprofiling.

Pilot Clinical Studies

Correlation of tumor biopsy vs. circulating DNA somatic mutations

A pilot study was initiated on human samples across different cancertypes. The concordance of tumor mutation profiles derived fromcirculating cell-free DNA with those derived from matched tumor biopsysamples was investigated. Higher than 93% concordance between tumor andcfDNA somatic mutation profiles in both colorectal and melanoma cancersacross 14 patients was found (Table 1).

TABLE 1 Mutant Genes in Percentage of mutant Patient ID Stage MatchedTumor cfDNA CRC #1 II-B TP53 0.2% CRC #2 II-C KRAS 0.6% SMAD4 1.5% GNAS1.4% FBXW7 0.8% CRC #3 III-B KRAS 1.1% TP53 1.4% PIK3CA 1.7% APC 0.7%CRC #4 III-B KRAS 0.3% TP53 0.4% CRC #5 III-B KRAS 0.04% CRC #6 III-CKRAS 0.03% CRC #7 IV PIK3CA 1.3% KRAS 0.6% TP53 0.8% CRC #8 IV APC 0.3%SMO 0.6% TP53 0.4% KRAS 0.0% CRC #9 IV APC 47.3% APC 40.2% KRAS 37.7%PTEN 0.0% TP53 12.9% CRC #10 IV TP53 0.9% Melanoma #1 IV BRAF 0.2%Melanoma #2 IV APC 0.3% EGFR 0.9% MYC 10.5% Melanoma #3 IV BRAF 3.3%Melanoma #4 IV BRAF 0.7%

It should be understood from the foregoing that, while particularimplementations have been illustrated and described, variousmodifications can be made thereto and are contemplated herein. It isalso not intended that the invention be limited by the specific examplesprovided within the specification. While the invention has beendescribed with reference to the aforementioned specification, thedescriptions and illustrations of the preferable embodiments herein arenot meant to be construed in a limiting sense. Furthermore, it shall beunderstood that all aspects of the invention are not limited to thespecific depictions, configurations or relative proportions set forthherein which depend upon a variety of conditions and variables. Variousmodifications in form and detail of the embodiments of the inventionwill be apparent to a person skilled in the art. It is thereforecontemplated that the invention shall also cover any such modifications,variations and equivalents.

What is claimed is:
 1. A method, comprising: providing initial startinggenetic material obtained from a bodily sample obtained from a subject;converting double stranded polynucleotides from the initial startinggenetic material into at least one set of non-uniquely tagged parentpolynucleotides, wherein each polynucleotide in a set is mappable to areference sequence; and for each set of tagged parent polynucleotides:i. amplifying the tagged parent polynucleotides in the set to produce acorresponding set of amplified progeny polynucleotides; ii. sequencingthe set of amplified progeny polynucleotides to produce a set ofsequencing reads; iii. collapsing the set of sequencing reads togenerate a set of consensus sequences, wherein collapsing uses sequenceinformation from a tag and at least one of: sequence information at abeginning region of a sequence read, an end region of the sequence readand length of the sequence read, each consensus sequence correspondingto a unique polynucleotide among the set of tagged parentpolynucleotides; and ii. analyzing the set of consensus sequences foreach set of tagged parent molecules separately or in combination.
 2. Themethod of claim 1, wherein analyzing comprises detecting mutations,indels, copy number variations, transversions, translocations,inversion, deletions, aneuploidy, partial aneuploidy, polyploidy,chromosomal instability, chromosomal structure alterations, genefusions, chromosome fusions, gene truncations, gene amplification, geneduplications, chromosomal lesions, DNA lesions, abnormal changes innucleic acid chemical modifications, abnormal changes in epigeneticpatterns, abnormal changes in nucleic acid methylation infection orcancer.
 3. The method of claim 1, wherein analyzing comprises detectingthe presence of sequence variations in the set of consensus sequencescompared with the reference sequence.
 4. The method of claim 3, whereinthe mappable position in the reference sequence is the locus of a tumormarker, and wherein detecting the presence of sequence variationscomprises detecting the tumor marker in the set of consensus sequences.5. The method of claim 4, further comprising detecting the presence ofsequence variations for a panel of oncogenes.
 6. The method of claim 1,wherein the at least one set of non-uniquely tagged parentpolynucleotides is a plurality of sets, and wherein the analyzingcomprises normalizing copy number variation of consensus sequencesbetween different mappable reference sequences.
 7. The method of claim1, wherein collapsing also uses information about length of eachsequence read.
 8. The method of claim 1, comprising filtering out readsthat fail to meet a set threshold.
 9. The method of claim 1, wherein theinitial starting genetic material is tagged with between 5 and 100different identifiers.
 10. The method of claim 1, wherein the initialstarting genetic material comprises between about 10 ng and 10 μg offragmented polynucleotides.
 11. The method of claim 1, wherein theinitial starting genetic material comprises no more than 100 ng ofpolynucleotides.
 12. The method of claim 1, wherein converting comprisesblunt-end ligation or sticky end ligation.
 13. The method of claim 1,further comprising bottlenecking the initial starting genetic materialprior to converting.
 14. The method of claim 1, further comprisingconverting the initial starting genetic material into tagged parentpolynucleotides with a conversion efficiency of at least 10%.
 15. Themethod of claim 1, further comprising converting the initial startinggenetic material into tagged parent polynucleotides with a conversionefficiency of at least 30%.
 16. The method of claim 1, furthercomprising converting the initial starting genetic material into taggedparent polynucleotides with a conversion efficiency of at least 50%. 17.The method of claim 1, wherein the initial starting genetic material iscell-free nucleic acid.
 18. The method of claim 17 wherein analyzingcomprises: (a) identifying a subset of mapped sequence reads that alignwith a variant of the reference sequence at each mappable base position;(b) for each mappable base position, calculating a ratio of (1) a numberof mapped sequence reads that include a variant as compared to thereference sequence, to (2) a number of total sequence reads for eachmappable base position; (c) normalizing the ratios or frequency ofvariance for each mappable base position and determining potential rarevariant(s) or mutation(s); and (d) comparing the resulting number foreach of the regions with potential rare variant(s) or mutation(s) tosimilarly derived numbers from a reference sample.
 19. The method ofclaim 18, wherein the prevalence/concentration of each rare variantidentified in the subject is reported and quantified simultaneously. 20.The method of claim 18, wherein a confidence score regarding theprevalence/concentrations of rare variants in the subject is reported.21. The method of claim 18, wherein the normalizing is performed usingone or more of hidden Markov, dynamic programming, support vectormachine, Bayesian or probabilistic modeling, trellis decoding, Viterbidecoding, expectation maximization, Kalman filtering, or neural networkmethodologies.
 22. The method of claim 18, wherein the genetic profileof a tumor, infection or other tissue abnormality is inferred.
 23. Themethod of claim 17 wherein analyzing comprises: (a) quantifying orenumerating mapped reads in two or more predefined regions of thereference sequence; and (b) determining copy number variation in one ormore of the predefined regions by: (1) normalizing number of uniquesequence reads in the predefined regions to one another; (2) comparingthe normalized numbers obtained in (1) to normalized numbers obtainedfrom a control sample.
 24. The method of claim 1, wherein collapsingcomprises: grouping sequence reads sequenced from amplified progenypolynucleotides into families, each family amplified from the sametagged parent polynucleotide; and determining a consensus sequence basedon sequence reads in a family.
 25. The method of claim 1, wherein theinitial starting genetic material is tagged with a DNA barcode.
 26. Themethod of claim 1, wherein initial starting genetic material is taggedwith polynucleotide barcodes on both ends of a parent polynucleotide byligation prior to amplification.
 27. The method of claim 1, furthercomprising selectively enriching regions from the subject's genome ortranscriptome prior to sequencing.
 28. The method of claim 1, furthercomprising non-selectively enriching regions from the subject's genomeor transcriptome prior to sequencing.
 29. The method of claim 1, whereincollapsing uses sequence information from a tag and more than one of:sequence information at a beginning region of a sequence read, the endregion of the sequence read, and the length of the sequence read. 30.The method of claim 1, wherein sequence information from a tag comprisessequence information from barcodes attached at both ends of a doublestranded polynucleotide molecule in the starting genetic material.